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Merge branch 'main' into rtdl/docx_latex

Browse Source 
Signed-off-by: Rafael Teixeira de Lima <Rafael.td.lima@gmail.com>
This commit is contained in:
Rafael Teixeira de Lima 2025-03-04 16:51:53 +01:00 committed by GitHub
commit 5630c6b8fd
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304 changed files with 37555 additions and 36823 deletions
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11
.github/workflows/checks.yml vendored
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@ -1,6 +1,10 @@
on:
workflow_call:
env:
HF_HUB_DOWNLOAD_TIMEOUT: "60"
HF_HUB_ETAG_TIMEOUT: "60"
jobs:
run-checks:
runs-on: ubuntu-latest
@ -14,6 +18,11 @@ jobs:
- name: Set TESSDATA_PREFIX
run: |
echo "TESSDATA_PREFIX=$(dpkg -L tesseract-ocr-eng | grep tessdata$)" >> "$GITHUB_ENV"
- name: Cache Hugging Face models
uses: actions/cache@v4
with:
path: ~/.cache/huggingface
key: huggingface-cache-py${{ matrix.python-version }}
- uses: ./.github/actions/setup-poetry
with:
python-version: ${{ matrix.python-version }}
@ -28,7 +37,7 @@ jobs:
run: |
for file in docs/examples/*.py; do
# Skip batch_convert.py
if [[ "$(basename "$file")" =~ ^(batch_convert|minimal|export_multimodal|custom_convert|develop_picture_enrichment).py ]]; then
if [[ "$(basename "$file")" =~ ^(batch_convert|minimal_vlm_pipeline|minimal|export_multimodal|custom_convert|develop_picture_enrichment|rapidocr_with_custom_models|offline_convert|pictures_description|pictures_description_api).py ]]; then
echo "Skipping $file"
continue
fi

6
.github/workflows/docs.yml vendored
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@ -14,10 +14,6 @@ jobs:
- uses: ./.github/actions/setup-poetry
- name: Build docs
run: poetry run mkdocs build --verbose --clean
- name: Make docs LLM ready
if: inputs.deploy
uses: demodrive-ai/llms-txt-action@ad720693843126e6a73910a667d0eba37c1dea4b
- name: Build and push docs
if: inputs.deploy
run: poetry run mkdocs gh-deploy --force --dirty
run: poetry run mkdocs gh-deploy --force

131
CHANGELOG.md
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@ -1,3 +1,134 @@
## [v2.25.1](https://github.com/DS4SD/docling/releases/tag/v2.25.1) - 2025-03-03
### Fix
* Enable locks for threadsafe pdfium ([#1052](https://github.com/DS4SD/docling/issues/1052)) ([`8dc0562`](https://github.com/DS4SD/docling/commit/8dc0562542299cf972d14eeeb4393e50b589c8ad))
* **html:** Use 'start' attribute when parsing ordered lists from HTML docs ([#1062](https://github.com/DS4SD/docling/issues/1062)) ([`de7b963`](https://github.com/DS4SD/docling/commit/de7b963b09a34916f0a8d99649269aeb37db1408))
### Documentation
* Improve docs on token limit warning triggered by HybridChunker ([#1077](https://github.com/DS4SD/docling/issues/1077)) ([`db3ceef`](https://github.com/DS4SD/docling/commit/db3ceefd4ae6251a97e333bcb03051698b3fa71a))
## [v2.25.0](https://github.com/DS4SD/docling/releases/tag/v2.25.0) - 2025-02-26
### Feature
* [Experimental] Introduce VLM pipeline using HF AutoModelForVision2Seq, featuring SmolDocling model ([#1054](https://github.com/DS4SD/docling/issues/1054)) ([`3c9fe76`](https://github.com/DS4SD/docling/commit/3c9fe76b706b7714b25d49cb09050c42e3b8c849))
* **cli:** Add option for downloading all models, refine help messages ([#1061](https://github.com/DS4SD/docling/issues/1061)) ([`ab683e4`](https://github.com/DS4SD/docling/commit/ab683e4fb6df4973d2efda04f00c269a2dc95f5b))
### Fix
* Vlm using artifacts path ([#1057](https://github.com/DS4SD/docling/issues/1057)) ([`e197225`](https://github.com/DS4SD/docling/commit/e1972257399151503d60b4806976c8b9b6911aa8))
* **html:** Parse text in div elements as TextItem ([#1041](https://github.com/DS4SD/docling/issues/1041)) ([`1b0ead6`](https://github.com/DS4SD/docling/commit/1b0ead69078030a0e4d25b51450ef2aa4a2e79fc))
### Documentation
* Extend chunking docs, add FAQ on token limit ([#1053](https://github.com/DS4SD/docling/issues/1053)) ([`c84b973`](https://github.com/DS4SD/docling/commit/c84b973959a254db22ac9a7dc8810628e4808a2d))
## [v2.24.0](https://github.com/DS4SD/docling/releases/tag/v2.24.0) - 2025-02-20
### Feature
* Implement new reading-order model ([#916](https://github.com/DS4SD/docling/issues/916)) ([`c93e369`](https://github.com/DS4SD/docling/commit/c93e36988f1e1e461477223143c2c1fb2162d11f))
## [v2.23.1](https://github.com/DS4SD/docling/releases/tag/v2.23.1) - 2025-02-20
### Fix
* Runtime error when Pandas Series is not always of string type ([#1024](https://github.com/DS4SD/docling/issues/1024)) ([`6796f0a`](https://github.com/DS4SD/docling/commit/6796f0a13263281cd48712b3c71579bfd81bb0d1))
### Documentation
* Revamp picture description example ([#1015](https://github.com/DS4SD/docling/issues/1015)) ([`27c0400`](https://github.com/DS4SD/docling/commit/27c04007bc1be7a6f6c90aaf04ea9f4ff8eb1f3d))
## [v2.23.0](https://github.com/DS4SD/docling/releases/tag/v2.23.0) - 2025-02-17
### Feature
* Support cuda:n GPU device allocation ([#694](https://github.com/DS4SD/docling/issues/694)) ([`77eb77b`](https://github.com/DS4SD/docling/commit/77eb77bdc2c07b632a1d171826d1855a5218399e))
* **xml-jats:** Parse XML JATS documents ([#967](https://github.com/DS4SD/docling/issues/967)) ([`428b656`](https://github.com/DS4SD/docling/commit/428b656793cb75d108c69f20c254be7c198cee5c))
### Fix
* Revise DocTags, fix iterate_items to output content_layer in items ([#965](https://github.com/DS4SD/docling/issues/965)) ([`6e75f0b`](https://github.com/DS4SD/docling/commit/6e75f0b5d3ee42738a80049d4cf2fa6d34e8ab97))
## [v2.22.0](https://github.com/DS4SD/docling/releases/tag/v2.22.0) - 2025-02-14
### Feature
* Add support for CSV input with new backend to transform CSV files to DoclingDocument ([#945](https://github.com/DS4SD/docling/issues/945)) ([`00d9405`](https://github.com/DS4SD/docling/commit/00d9405b0ac519d321ae54e8150f5facbaabbe14))
* Introduce the enable_remote_services option to allow remote connections while processing ([#941](https://github.com/DS4SD/docling/issues/941)) ([`2716c7d`](https://github.com/DS4SD/docling/commit/2716c7d4ffb836664178178d3f8d01b7f9112595))
* Allow artifacts_path to be defined as ENV ([#940](https://github.com/DS4SD/docling/issues/940)) ([`5101e25`](https://github.com/DS4SD/docling/commit/5101e2519e7a5bb727531b1412b1131a7cfbda52))
### Fix
* Update Pillow constraints ([#958](https://github.com/DS4SD/docling/issues/958)) ([`af19c03`](https://github.com/DS4SD/docling/commit/af19c03f6e5e0b24e12d6a3baac6c46a4c8b10d1))
* Fix the initialization of the TesseractOcrModel ([#935](https://github.com/DS4SD/docling/issues/935)) ([`c47ae70`](https://github.com/DS4SD/docling/commit/c47ae700ece2ea4efee17f82e4667c1ce9a0ed2a))
### Documentation
* Update example Dockerfile with download CLI ([#929](https://github.com/DS4SD/docling/issues/929)) ([`7493d5b`](https://github.com/DS4SD/docling/commit/7493d5b01f8be60294afeffdfb54a62bb74bcc92))
* Examples for picture descriptions ([#951](https://github.com/DS4SD/docling/issues/951)) ([`2d66e99`](https://github.com/DS4SD/docling/commit/2d66e99b69f39a282109c366fae3679f41c6e081))
## [v2.21.0](https://github.com/DS4SD/docling/releases/tag/v2.21.0) - 2025-02-10
### Feature
* Add content_layer property to items to address body, furniture and other roles ([#735](https://github.com/DS4SD/docling/issues/735)) ([`cf78d5b`](https://github.com/DS4SD/docling/commit/cf78d5b7b9f12728270e673857fd299efc01a7db))
## [v2.20.0](https://github.com/DS4SD/docling/releases/tag/v2.20.0) - 2025-02-07
### Feature
* Describe pictures using vision models ([#259](https://github.com/DS4SD/docling/issues/259)) ([`4cc6e3e`](https://github.com/DS4SD/docling/commit/4cc6e3ea5e858b367136acc729b723ea0552d22a))
### Fix
* Remove unused httpx ([#919](https://github.com/DS4SD/docling/issues/919)) ([`c18f47c`](https://github.com/DS4SD/docling/commit/c18f47c5c032c49bf3175aecd2236df37c0e9ae1))
## [v2.19.0](https://github.com/DS4SD/docling/releases/tag/v2.19.0) - 2025-02-07
### Feature
* New artifacts path and CLI utility ([#876](https://github.com/DS4SD/docling/issues/876)) ([`ed74fe2`](https://github.com/DS4SD/docling/commit/ed74fe2ec0a702834f0deacfdb5717c8c587dab1))
### Fix
* **markdown:** Handle nested lists ([#910](https://github.com/DS4SD/docling/issues/910)) ([`90b766e`](https://github.com/DS4SD/docling/commit/90b766e2ae1695a759191df37c272efc09be5ee3))
* Test cases for RTL programmatic PDFs and fixes for the formula model ([#903](https://github.com/DS4SD/docling/issues/903)) ([`9114ada`](https://github.com/DS4SD/docling/commit/9114ada7bc4dd45ce0046de2f9d00a80ccb25c79))
* **msword_backend:** Handle conversion error in label parsing ([#896](https://github.com/DS4SD/docling/issues/896)) ([`722a6eb`](https://github.com/DS4SD/docling/commit/722a6eb7b994a0261312a356df80b2fced121812))
* Enrichment models batch size and expose picture classifier ([#878](https://github.com/DS4SD/docling/issues/878)) ([`5ad6de0`](https://github.com/DS4SD/docling/commit/5ad6de05600315617b574bd12af553e00b4d316e))
### Documentation
* Introduce example with custom models for RapidOCR ([#874](https://github.com/DS4SD/docling/issues/874)) ([`6d3fea0`](https://github.com/DS4SD/docling/commit/6d3fea019635bd6ca94bd36c3928b28c245d638d))
## [v2.18.0](https://github.com/DS4SD/docling/releases/tag/v2.18.0) - 2025-02-03
### Feature
* Expose equation exports ([#869](https://github.com/DS4SD/docling/issues/869)) ([`6a76b49`](https://github.com/DS4SD/docling/commit/6a76b49a4756fd00503d0baec5db8d23be8207e8))
* Add option to define page range ([#852](https://github.com/DS4SD/docling/issues/852)) ([`70d68b6`](https://github.com/DS4SD/docling/commit/70d68b6164c6c7029b39dd65c5a278278768c381))
* **docx:** Support of SDTs in docx backend ([#853](https://github.com/DS4SD/docling/issues/853)) ([`d727b04`](https://github.com/DS4SD/docling/commit/d727b04ad080df0b3811902059e0fe0539f7037e))
* Python 3.13 support ([#841](https://github.com/DS4SD/docling/issues/841)) ([`4df085a`](https://github.com/DS4SD/docling/commit/4df085aa6c6f5cc043f4f7a9f0c1b4af43f95e8f))
### Fix
* **markdown:** Fix parsing if doc ending with table ([#873](https://github.com/DS4SD/docling/issues/873)) ([`5ac2887`](https://github.com/DS4SD/docling/commit/5ac2887e4ad52ed6e7147e3af1e3ee5eb0006a70))
* **markdown:** Add support for HTML content ([#855](https://github.com/DS4SD/docling/issues/855)) ([`94751a7`](https://github.com/DS4SD/docling/commit/94751a78f4f61b78f64952190717440ec6d84c62))
* **docx:** Merged table cells not properly converted ([#857](https://github.com/DS4SD/docling/issues/857)) ([`0cd81a8`](https://github.com/DS4SD/docling/commit/0cd81a81226c0d4aa4f20e4e58c3b33e4fe50ce0))
* Processing of placeholder shapes in pptx that have text but no bbox ([#868](https://github.com/DS4SD/docling/issues/868)) ([`eff16b6`](https://github.com/DS4SD/docling/commit/eff16b62ccdb0eb764eeacee550563898784dd6a))
* KeyError in tableformer prediction ([#854](https://github.com/DS4SD/docling/issues/854)) ([`b1cf796`](https://github.com/DS4SD/docling/commit/b1cf796730901222ad0882ff44efa0ef43a743ee))
* Fixed docx import with headers that are also lists ([#842](https://github.com/DS4SD/docling/issues/842)) ([`2c037ae`](https://github.com/DS4SD/docling/commit/2c037ae62e123967eddf065ccb2abbaf78cdcab3))
* Use new add_code in html backend and add more typing hints ([#850](https://github.com/DS4SD/docling/issues/850)) ([`2a1f8af`](https://github.com/DS4SD/docling/commit/2a1f8afe7e8d9d508aebcfd3998ee1625c938933))
* **markdown:** Fix empty block handling ([#843](https://github.com/DS4SD/docling/issues/843)) ([`bccb022`](https://github.com/DS4SD/docling/commit/bccb022fc82d4d0ef2ed2d8bea5f5d8e6400c1d9))
* Fix for the crash when encountering WMF images in pptx and docx ([#837](https://github.com/DS4SD/docling/issues/837)) ([`fea0a99`](https://github.com/DS4SD/docling/commit/fea0a99a95d97e72687f48f8174d31102655483e))
### Documentation
* Updated the readme with upcoming features ([#831](https://github.com/DS4SD/docling/issues/831)) ([`d7c0828`](https://github.com/DS4SD/docling/commit/d7c082894e3ef85881665d20167198adcbc1becd))
* Add example for inspection of picture content ([#624](https://github.com/DS4SD/docling/issues/624)) ([`f9144f2`](https://github.com/DS4SD/docling/commit/f9144f2bb6b322244c9d37683dca1e537ec6d781))
## [v2.17.0](https://github.com/DS4SD/docling/releases/tag/v2.17.0) - 2025-01-28
### Feature

6
Dockerfile
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@ -16,8 +16,7 @@ ENV TORCH_HOME=/tmp/
COPY docs/examples/minimal.py /root/minimal.py
RUN python -c 'from deepsearch_glm.utils.load_pretrained_models import load_pretrained_nlp_models; load_pretrained_nlp_models(verbose=True);'
RUN python -c 'from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline; StandardPdfPipeline.download_models_hf(force=True);'
RUN docling-tools models download
# On container environments, always set a thread budget to avoid undesired thread congestion.
ENV OMP_NUM_THREADS=4
@ -25,3 +24,6 @@ ENV OMP_NUM_THREADS=4
# On container shell:
# > cd /root/
# > python minimal.py
# Running as `docker run -e DOCLING_ARTIFACTS_PATH=/root/.cache/docling/models` will use the
# model weights included in the container image.

2
README.md
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@ -123,6 +123,6 @@ For individual model usage, please refer to the model licenses found in the orig
Docling has been brought to you by IBM.
[supported_formats]: https://ds4sd.github.io/docling/supported_formats/
[supported_formats]: https://ds4sd.github.io/docling/usage/supported_formats/
[docling_document]: https://ds4sd.github.io/docling/concepts/docling_document/
[integrations]: https://ds4sd.github.io/docling/integrations/

125
docling/backend/csv_backend.py Normal file
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@ -0,0 +1,125 @@
import csv
import logging
import warnings
from io import BytesIO, StringIO
from pathlib import Path
from typing import Set, Union
from docling_core.types.doc import DoclingDocument, DocumentOrigin, TableCell, TableData
from docling.backend.abstract_backend import DeclarativeDocumentBackend
from docling.datamodel.base_models import InputFormat
from docling.datamodel.document import InputDocument
_log = logging.getLogger(__name__)
class CsvDocumentBackend(DeclarativeDocumentBackend):
content: StringIO
def __init__(self, in_doc: "InputDocument", path_or_stream: Union[BytesIO, Path]):
super().__init__(in_doc, path_or_stream)
# Load content
try:
if isinstance(self.path_or_stream, BytesIO):
self.content = StringIO(self.path_or_stream.getvalue().decode("utf-8"))
elif isinstance(self.path_or_stream, Path):
self.content = StringIO(self.path_or_stream.read_text("utf-8"))
self.valid = True
except Exception as e:
raise RuntimeError(
f"CsvDocumentBackend could not load document with hash {self.document_hash}"
) from e
return
def is_valid(self) -> bool:
return self.valid
@classmethod
def supports_pagination(cls) -> bool:
return False
def unload(self):
if isinstance(self.path_or_stream, BytesIO):
self.path_or_stream.close()
self.path_or_stream = None
@classmethod
def supported_formats(cls) -> Set[InputFormat]:
return {InputFormat.CSV}
def convert(self) -> DoclingDocument:
"""
Parses the CSV data into a structured document model.
"""
# Detect CSV dialect
head = self.content.readline()
dialect = csv.Sniffer().sniff(head, ",;\t|:")
_log.info(f'Parsing CSV with delimiter: "{dialect.delimiter}"')
if not dialect.delimiter in {",", ";", "\t", "|", ":"}:
raise RuntimeError(
f"Cannot convert csv with unknown delimiter {dialect.delimiter}."
)
# Parce CSV
self.content.seek(0)
result = csv.reader(self.content, dialect=dialect, strict=True)
self.csv_data = list(result)
_log.info(f"Detected {len(self.csv_data)} lines")
# Ensure uniform column length
expected_length = len(self.csv_data[0])
is_uniform = all(len(row) == expected_length for row in self.csv_data)
if not is_uniform:
warnings.warn(
f"Inconsistent column lengths detected in CSV data. "
f"Expected {expected_length} columns, but found rows with varying lengths. "
f"Ensure all rows have the same number of columns."
)
# Parse the CSV into a structured document model
origin = DocumentOrigin(
filename=self.file.name or "file.csv",
mimetype="text/csv",
binary_hash=self.document_hash,
)
doc = DoclingDocument(name=self.file.stem or "file.csv", origin=origin)
if self.is_valid():
# Convert CSV data to table
if self.csv_data:
num_rows = len(self.csv_data)
num_cols = max(len(row) for row in self.csv_data)
table_data = TableData(
num_rows=num_rows,
num_cols=num_cols,
table_cells=[],
)
# Convert each cell to TableCell
for row_idx, row in enumerate(self.csv_data):
for col_idx, cell_value in enumerate(row):
cell = TableCell(
text=str(cell_value),
row_span=1, # CSV doesn't support merged cells
col_span=1,
start_row_offset_idx=row_idx,
end_row_offset_idx=row_idx + 1,
start_col_offset_idx=col_idx,
end_col_offset_idx=col_idx + 1,
col_header=row_idx == 0, # First row as header
row_header=False,
)
table_data.table_cells.append(cell)
doc.add_table(data=table_data)
else:
raise RuntimeError(
f"Cannot convert doc with {self.document_hash} because the backend failed to init."
)
return doc

68
docling/backend/docling_parse_v2_backend.py
View File

@ -12,6 +12,7 @@ from pypdfium2 import PdfPage
from docling.backend.pdf_backend import PdfDocumentBackend, PdfPageBackend
from docling.datamodel.base_models import Cell, Size
from docling.utils.locks import pypdfium2_lock
if TYPE_CHECKING:
from docling.datamodel.document import InputDocument
@ -182,20 +183,24 @@ class DoclingParseV2PageBackend(PdfPageBackend):
padbox.r = page_size.width - padbox.r
padbox.t = page_size.height - padbox.t
image = (
self._ppage.render(
scale=scale * 1.5,
rotation=0, # no additional rotation
crop=padbox.as_tuple(),
)
.to_pil()
.resize(size=(round(cropbox.width * scale), round(cropbox.height * scale)))
) # We resize the image from 1.5x the given scale to make it sharper.
with pypdfium2_lock:
image = (
self._ppage.render(
scale=scale * 1.5,
rotation=0, # no additional rotation
crop=padbox.as_tuple(),
)
.to_pil()
.resize(
size=(round(cropbox.width * scale), round(cropbox.height * scale))
)
) # We resize the image from 1.5x the given scale to make it sharper.
return image
def get_size(self) -> Size:
return Size(width=self._ppage.get_width(), height=self._ppage.get_height())
with pypdfium2_lock:
return Size(width=self._ppage.get_width(), height=self._ppage.get_height())
def unload(self):
self._ppage = None
@ -206,23 +211,24 @@ class DoclingParseV2DocumentBackend(PdfDocumentBackend):
def __init__(self, in_doc: "InputDocument", path_or_stream: Union[BytesIO, Path]):
super().__init__(in_doc, path_or_stream)
self._pdoc = pdfium.PdfDocument(self.path_or_stream)
self.parser = pdf_parser_v2("fatal")
with pypdfium2_lock:
self._pdoc = pdfium.PdfDocument(self.path_or_stream)
self.parser = pdf_parser_v2("fatal")
success = False
if isinstance(self.path_or_stream, BytesIO):
success = self.parser.load_document_from_bytesio(
self.document_hash, self.path_or_stream
)
elif isinstance(self.path_or_stream, Path):
success = self.parser.load_document(
self.document_hash, str(self.path_or_stream)
)
success = False
if isinstance(self.path_or_stream, BytesIO):
success = self.parser.load_document_from_bytesio(
self.document_hash, self.path_or_stream
)
elif isinstance(self.path_or_stream, Path):
success = self.parser.load_document(
self.document_hash, str(self.path_or_stream)
)
if not success:
raise RuntimeError(
f"docling-parse v2 could not load document {self.document_hash}."
)
if not success:
raise RuntimeError(
f"docling-parse v2 could not load document {self.document_hash}."
)
def page_count(self) -> int:
# return len(self._pdoc) # To be replaced with docling-parse API
@ -236,9 +242,10 @@ class DoclingParseV2DocumentBackend(PdfDocumentBackend):
return len_2
def load_page(self, page_no: int) -> DoclingParseV2PageBackend:
return DoclingParseV2PageBackend(
self.parser, self.document_hash, page_no, self._pdoc[page_no]
)
with pypdfium2_lock:
return DoclingParseV2PageBackend(
self.parser, self.document_hash, page_no, self._pdoc[page_no]
)
def is_valid(self) -> bool:
return self.page_count() > 0
@ -246,5 +253,6 @@ class DoclingParseV2DocumentBackend(PdfDocumentBackend):
def unload(self):
super().unload()
self.parser.unload_document(self.document_hash)
self._pdoc.close()
self._pdoc = None
with pypdfium2_lock:
self._pdoc.close()
self._pdoc = None

418
docling/backend/html_backend.py
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@ -1,17 +1,22 @@
import logging
from io import BytesIO
from pathlib import Path
from typing import Optional, Set, Union
from typing import Final, Optional, Union, cast
from bs4 import BeautifulSoup, Tag
from bs4 import BeautifulSoup, NavigableString, PageElement, Tag
from bs4.element import PreformattedString
from docling_core.types.doc import (
DocItem,
DocItemLabel,
DoclingDocument,
DocumentOrigin,
GroupItem,
GroupLabel,
TableCell,
TableData,
)
from docling_core.types.doc.document import ContentLayer
from typing_extensions import override
from docling.backend.abstract_backend import DeclarativeDocumentBackend
from docling.datamodel.base_models import InputFormat
@ -19,21 +24,38 @@ from docling.datamodel.document import InputDocument
_log = logging.getLogger(__name__)
# tags that generate NodeItem elements
TAGS_FOR_NODE_ITEMS: Final = [
"h1",
"h2",
"h3",
"h4",
"h5",
"h6",
"p",
"pre",
"ul",
"ol",
"li",
"table",
"figure",
"img",
]
class HTMLDocumentBackend(DeclarativeDocumentBackend):
@override
def __init__(self, in_doc: "InputDocument", path_or_stream: Union[BytesIO, Path]):
super().__init__(in_doc, path_or_stream)
_log.debug("About to init HTML backend...")
self.soup: Optional[Tag] = None
# HTML file:
self.path_or_stream = path_or_stream
# Initialise the parents for the hierarchy
self.max_levels = 10
self.level = 0
self.parents = {} # type: ignore
self.parents: dict[int, Optional[Union[DocItem, GroupItem]]] = {}
for i in range(0, self.max_levels):
self.parents[i] = None
self.labels = {} # type: ignore
try:
if isinstance(self.path_or_stream, BytesIO):
@ -45,16 +67,20 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
self.soup = BeautifulSoup(html_content, "html.parser")
except Exception as e:
raise RuntimeError(
f"Could not initialize HTML backend for file with hash {self.document_hash}."
"Could not initialize HTML backend for file with "
f"hash {self.document_hash}."
) from e
@override
def is_valid(self) -> bool:
return self.soup is not None
@classmethod
@override
def supports_pagination(cls) -> bool:
return False
@override
def unload(self):
if isinstance(self.path_or_stream, BytesIO):
self.path_or_stream.close()
@ -62,9 +88,11 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
self.path_or_stream = None
@classmethod
def supported_formats(cls) -> Set[InputFormat]:
@override
def supported_formats(cls) -> set[InputFormat]:
return {InputFormat.HTML}
@override
def convert(self) -> DoclingDocument:
# access self.path_or_stream to load stuff
origin = DocumentOrigin(
@ -80,107 +108,116 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
assert self.soup is not None
content = self.soup.body or self.soup
# Replace <br> tags with newline characters
for br in content.find_all("br"):
br.replace_with("\n")
doc = self.walk(content, doc)
# TODO: remove style to avoid losing text from tags like i, b, span, ...
for br in content("br"):
br.replace_with(NavigableString("\n"))
headers = content.find(["h1", "h2", "h3", "h4", "h5", "h6"])
self.content_layer = (
ContentLayer.BODY if headers is None else ContentLayer.FURNITURE
)
self.walk(content, doc)
else:
raise RuntimeError(
f"Cannot convert doc with {self.document_hash} because the backend failed to init."
f"Cannot convert doc with {self.document_hash} because the backend "
"failed to init."
)
return doc
def walk(self, element: Tag, doc: DoclingDocument):
try:
# Iterate over elements in the body of the document
for idx, element in enumerate(element.children):
def walk(self, tag: Tag, doc: DoclingDocument) -> None:
# Iterate over elements in the body of the document
text: str = ""
for element in tag.children:
if isinstance(element, Tag):
try:
self.analyse_element(element, idx, doc)
self.analyze_tag(cast(Tag, element), doc)
except Exception as exc_child:
_log.error(" -> error treating child: ", exc_child)
_log.error(" => element: ", element, "\n")
_log.error(
f"Error processing child from tag{tag.name}: {exc_child}"
)
raise exc_child
elif isinstance(element, NavigableString) and not isinstance(
element, PreformattedString
):
# Floating text outside paragraphs or analyzed tags
text += element
siblings: list[Tag] = [
item for item in element.next_siblings if isinstance(item, Tag)
]
if element.next_sibling is None or any(
[item.name in TAGS_FOR_NODE_ITEMS for item in siblings]
):
text = text.strip()
if text and tag.name in ["div"]:
doc.add_text(
parent=self.parents[self.level],
label=DocItemLabel.TEXT,
text=text,
content_layer=self.content_layer,
)
text = ""
except Exception as exc:
pass
return
return doc
def analyse_element(self, element: Tag, idx: int, doc: DoclingDocument):
"""
if element.name!=None:
_log.debug("\t"*self.level, idx, "\t", f"{element.name} ({self.level})")
"""
if element.name in self.labels:
self.labels[element.name] += 1
def analyze_tag(self, tag: Tag, doc: DoclingDocument) -> None:
if tag.name in ["h1", "h2", "h3", "h4", "h5", "h6"]:
self.handle_header(tag, doc)
elif tag.name in ["p"]:
self.handle_paragraph(tag, doc)
elif tag.name in ["pre"]:
self.handle_code(tag, doc)
elif tag.name in ["ul", "ol"]:
self.handle_list(tag, doc)
elif tag.name in ["li"]:
self.handle_list_item(tag, doc)
elif tag.name == "table":
self.handle_table(tag, doc)
elif tag.name == "figure":
self.handle_figure(tag, doc)
elif tag.name == "img":
self.handle_image(tag, doc)
else:
self.labels[element.name] = 1
self.walk(tag, doc)
if element.name in ["h1", "h2", "h3", "h4", "h5", "h6"]:
self.handle_header(element, idx, doc)
elif element.name in ["p"]:
self.handle_paragraph(element, idx, doc)
elif element.name in ["pre"]:
self.handle_code(element, idx, doc)
elif element.name in ["ul", "ol"]:
self.handle_list(element, idx, doc)
elif element.name in ["li"]:
self.handle_listitem(element, idx, doc)
elif element.name == "table":
self.handle_table(element, idx, doc)
elif element.name == "figure":
self.handle_figure(element, idx, doc)
elif element.name == "img":
self.handle_image(element, idx, doc)
else:
self.walk(element, doc)
def get_text(self, item: PageElement) -> str:
"""Get the text content of a tag."""
parts: list[str] = self.extract_text_recursively(item)
def get_direct_text(self, item: Tag):
"""Get the direct text of the <li> element (ignoring nested lists)."""
text = item.find(string=True, recursive=False)
if isinstance(text, str):
return text.strip()
return ""
return "".join(parts) + " "
# Function to recursively extract text from all child nodes
def extract_text_recursively(self, item: Tag):
result = []
def extract_text_recursively(self, item: PageElement) -> list[str]:
result: list[str] = []
if isinstance(item, str):
if isinstance(item, NavigableString):
return [item]
if item.name not in ["ul", "ol"]:
try:
# Iterate over the children (and their text and tails)
for child in item:
try:
# Recursively get the child's text content
result.extend(self.extract_text_recursively(child))
except:
pass
except:
_log.warn("item has no children")
pass
tag = cast(Tag, item)
if tag.name not in ["ul", "ol"]:
for child in tag:
# Recursively get the child's text content
result.extend(self.extract_text_recursively(child))
return "".join(result) + " "
return ["".join(result) + " "]
def handle_header(self, element: Tag, idx: int, doc: DoclingDocument):
def handle_header(self, element: Tag, doc: DoclingDocument) -> None:
"""Handles header tags (h1, h2, etc.)."""
hlevel = int(element.name.replace("h", ""))
slevel = hlevel - 1
label = DocItemLabel.SECTION_HEADER
text = element.text.strip()
if hlevel == 1:
for key, val in self.parents.items():
self.content_layer = ContentLayer.BODY
for key in self.parents.keys():
self.parents[key] = None
self.level = 1
self.parents[self.level] = doc.add_text(
parent=self.parents[0], label=DocItemLabel.TITLE, text=text
parent=self.parents[0],
label=DocItemLabel.TITLE,
text=text,
content_layer=self.content_layer,
)
else:
if hlevel > self.level:
@ -191,13 +228,14 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
name=f"header-{i}",
label=GroupLabel.SECTION,
parent=self.parents[i - 1],
content_layer=self.content_layer,
)
self.level = hlevel
elif hlevel < self.level:
# remove the tail
for key, val in self.parents.items():
for key in self.parents.keys():
if key > hlevel:
self.parents[key] = None
self.level = hlevel
@ -206,42 +244,58 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
parent=self.parents[hlevel - 1],
text=text,
level=hlevel,
content_layer=self.content_layer,
)
def handle_code(self, element: Tag, idx: int, doc: DoclingDocument):
def handle_code(self, element: Tag, doc: DoclingDocument) -> None:
"""Handles monospace code snippets (pre)."""
if element.text is None:
return
text = element.text.strip()
label = DocItemLabel.CODE
if len(text) == 0:
return
doc.add_code(parent=self.parents[self.level], text=text)
if text:
doc.add_code(
parent=self.parents[self.level],
text=text,
content_layer=self.content_layer,
)
def handle_paragraph(self, element: Tag, idx: int, doc: DoclingDocument):
def handle_paragraph(self, element: Tag, doc: DoclingDocument) -> None:
"""Handles paragraph tags (p)."""
if element.text is None:
return
text = element.text.strip()
label = DocItemLabel.PARAGRAPH
if len(text) == 0:
return
doc.add_text(parent=self.parents[self.level], label=label, text=text)
if text:
doc.add_text(
parent=self.parents[self.level],
label=DocItemLabel.TEXT,
text=text,
content_layer=self.content_layer,
)
def handle_list(self, element: Tag, idx: int, doc: DoclingDocument):
def handle_list(self, element: Tag, doc: DoclingDocument) -> None:
"""Handles list tags (ul, ol) and their list items."""
if element.name == "ul":
# create a list group
self.parents[self.level + 1] = doc.add_group(
parent=self.parents[self.level], name="list", label=GroupLabel.LIST
parent=self.parents[self.level],
name="list",
label=GroupLabel.LIST,
content_layer=self.content_layer,
)
elif element.name == "ol":
start_attr = element.get("start")
start: int = (
int(start_attr)
if isinstance(start_attr, str) and start_attr.isnumeric()
else 1
)
# create a list group
self.parents[self.level + 1] = doc.add_group(
parent=self.parents[self.level],
name="ordered list",
name="ordered list" + (f" start {start}" if start != 1 else ""),
label=GroupLabel.ORDERED_LIST,
content_layer=self.content_layer,
)
self.level += 1
@ -250,25 +304,36 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
self.parents[self.level + 1] = None
self.level -= 1
def handle_listitem(self, element: Tag, idx: int, doc: DoclingDocument):
"""Handles listitem tags (li)."""
nested_lists = element.find(["ul", "ol"])
def handle_list_item(self, element: Tag, doc: DoclingDocument) -> None:
"""Handles list item tags (li)."""
nested_list = element.find(["ul", "ol"])
parent_list_label = self.parents[self.level].label
index_in_list = len(self.parents[self.level].children) + 1
parent = self.parents[self.level]
if parent is None:
_log.debug(f"list-item has no parent in DoclingDocument: {element}")
return
parent_label: str = parent.label
index_in_list = len(parent.children) + 1
if (
parent_label == GroupLabel.ORDERED_LIST
and isinstance(parent, GroupItem)
and parent.name
):
start_in_list: str = parent.name.split(" ")[-1]
start: int = int(start_in_list) if start_in_list.isnumeric() else 1
index_in_list += start - 1
if nested_lists:
name = element.name
if nested_list:
# Text in list item can be hidden within hierarchy, hence
# we need to extract it recursively
text = self.extract_text_recursively(element)
text: str = self.get_text(element)
# Flatten text, remove break lines:
text = text.replace("\n", "").replace("\r", "")
text = " ".join(text.split()).strip()
marker = ""
enumerated = False
if parent_list_label == GroupLabel.ORDERED_LIST:
if parent_label == GroupLabel.ORDERED_LIST:
marker = str(index_in_list)
enumerated = True
@ -278,7 +343,8 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
text=text,
enumerated=enumerated,
marker=marker,
parent=self.parents[self.level],
parent=parent,
content_layer=self.content_layer,
)
self.level += 1
@ -287,74 +353,95 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
self.parents[self.level + 1] = None
self.level -= 1
elif isinstance(element.text, str):
elif element.text.strip():
text = element.text.strip()
marker = ""
enumerated = False
if parent_list_label == GroupLabel.ORDERED_LIST:
if parent_label == GroupLabel.ORDERED_LIST:
marker = f"{str(index_in_list)}."
enumerated = True
doc.add_list_item(
text=text,
enumerated=enumerated,
marker=marker,
parent=self.parents[self.level],
parent=parent,
content_layer=self.content_layer,
)
else:
_log.warn("list-item has no text: ", element)
def handle_table(self, element: Tag, idx: int, doc: DoclingDocument):
"""Handles table tags."""
_log.debug(f"list-item has no text: {element}")
@staticmethod
def parse_table_data(element: Tag) -> Optional[TableData]:
nested_tables = element.find("table")
if nested_tables is not None:
_log.warn("detected nested tables: skipping for now")
return
_log.debug("Skipping nested table.")
return None
# Count the number of rows (number of <tr> elements)
num_rows = len(element.find_all("tr"))
num_rows = len(element("tr"))
# Find the number of columns (taking into account colspan)
num_cols = 0
for row in element.find_all("tr"):
for row in element("tr"):
col_count = 0
for cell in row.find_all(["td", "th"]):
colspan = int(cell.get("colspan", 1))
if not isinstance(row, Tag):
continue
for cell in row(["td", "th"]):
if not isinstance(row, Tag):
continue
val = cast(Tag, cell).get("colspan", "1")
colspan = int(val) if (isinstance(val, str) and val.isnumeric()) else 1
col_count += colspan
num_cols = max(num_cols, col_count)
grid = [[None for _ in range(num_cols)] for _ in range(num_rows)]
grid: list = [[None for _ in range(num_cols)] for _ in range(num_rows)]
data = TableData(num_rows=num_rows, num_cols=num_cols, table_cells=[])
# Iterate over the rows in the table
for row_idx, row in enumerate(element.find_all("tr")):
for row_idx, row in enumerate(element("tr")):
if not isinstance(row, Tag):
continue
# For each row, find all the column cells (both <td> and <th>)
cells = row.find_all(["td", "th"])
cells = row(["td", "th"])
# Check if each cell in the row is a header -> means it is a column header
col_header = True
for j, html_cell in enumerate(cells):
if html_cell.name == "td":
for html_cell in cells:
if isinstance(html_cell, Tag) and html_cell.name == "td":
col_header = False
# Extract the text content of each cell
col_idx = 0
# Extract and print the text content of each cell
for _, html_cell in enumerate(cells):
for html_cell in cells:
if not isinstance(html_cell, Tag):
continue
# extract inline formulas
for formula in html_cell("inline-formula"):
math_parts = formula.text.split("$$")
if len(math_parts) == 3:
math_formula = f"$${math_parts[1]}$$"
formula.replace_with(NavigableString(math_formula))
# TODO: extract content correctly from table-cells with lists
text = html_cell.text
try:
text = self.extract_table_cell_text(html_cell)
except Exception as exc:
_log.warn("exception: ", exc)
exit(-1)
# label = html_cell.name
col_span = int(html_cell.get("colspan", 1))
row_span = int(html_cell.get("rowspan", 1))
col_val = html_cell.get("colspan", "1")
col_span = (
int(col_val)
if isinstance(col_val, str) and col_val.isnumeric()
else 1
)
row_val = html_cell.get("rowspan", "1")
row_span = (
int(row_val)
if isinstance(row_val, str) and row_val.isnumeric()
else 1
)
while grid[row_idx][col_idx] is not None:
col_idx += 1
@ -362,7 +449,7 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
for c in range(col_span):
grid[row_idx + r][col_idx + c] = text
cell = TableCell(
table_cell = TableCell(
text=text,
row_span=row_span,
col_span=col_span,
@ -373,70 +460,87 @@ class HTMLDocumentBackend(DeclarativeDocumentBackend):
col_header=col_header,
row_header=((not col_header) and html_cell.name == "th"),
)
data.table_cells.append(cell)
data.table_cells.append(table_cell)
doc.add_table(data=data, parent=self.parents[self.level])
return data
def get_list_text(self, list_element: Tag, level=0):
def handle_table(self, element: Tag, doc: DoclingDocument) -> None:
"""Handles table tags."""
table_data = HTMLDocumentBackend.parse_table_data(element)
if table_data is not None:
doc.add_table(
data=table_data,
parent=self.parents[self.level],
content_layer=self.content_layer,
)
def get_list_text(self, list_element: Tag, level: int = 0) -> list[str]:
"""Recursively extract text from <ul> or <ol> with proper indentation."""
result = []
bullet_char = "*" # Default bullet character for unordered lists
if list_element.name == "ol": # For ordered lists, use numbers
for i, li in enumerate(list_element.find_all("li", recursive=False), 1):
for i, li in enumerate(list_element("li", recursive=False), 1):
if not isinstance(li, Tag):
continue
# Add numbering for ordered lists
result.append(f"{' ' * level}{i}. {li.get_text(strip=True)}")
# Handle nested lists
nested_list = li.find(["ul", "ol"])
if nested_list:
if isinstance(nested_list, Tag):
result.extend(self.get_list_text(nested_list, level + 1))
elif list_element.name == "ul": # For unordered lists, use bullet points
for li in list_element.find_all("li", recursive=False):
for li in list_element("li", recursive=False):
if not isinstance(li, Tag):
continue
# Add bullet points for unordered lists
result.append(
f"{' ' * level}{bullet_char} {li.get_text(strip=True)}"
)
# Handle nested lists
nested_list = li.find(["ul", "ol"])
if nested_list:
if isinstance(nested_list, Tag):
result.extend(self.get_list_text(nested_list, level + 1))
return result
def extract_table_cell_text(self, cell: Tag):
"""Extract text from a table cell, including lists with indents."""
contains_lists = cell.find(["ul", "ol"])
if contains_lists is None:
return cell.text
else:
_log.debug(
"should extract the content correctly for table-cells with lists ..."
)
return cell.text
def handle_figure(self, element: Tag, idx: int, doc: DoclingDocument):
def handle_figure(self, element: Tag, doc: DoclingDocument) -> None:
"""Handles image tags (img)."""
# Extract the image URI from the <img> tag
# image_uri = root.xpath('//figure//img/@src')[0]
contains_captions = element.find(["figcaption"])
if contains_captions is None:
doc.add_picture(parent=self.parents[self.level], caption=None)
if not isinstance(contains_captions, Tag):
doc.add_picture(
parent=self.parents[self.level],
caption=None,
content_layer=self.content_layer,
)
else:
texts = []
for item in contains_captions:
texts.append(item.text)
fig_caption = doc.add_text(
label=DocItemLabel.CAPTION, text=("".join(texts)).strip()
label=DocItemLabel.CAPTION,
text=("".join(texts)).strip(),
content_layer=self.content_layer,
)
doc.add_picture(
parent=self.parents[self.level],
caption=fig_caption,
content_layer=self.content_layer,
)
def handle_image(self, element: Tag, idx, doc: DoclingDocument):
def handle_image(self, element: Tag, doc: DoclingDocument) -> None:
"""Handles image tags (img)."""
doc.add_picture(parent=self.parents[self.level], caption=None)
_log.debug(f"ignoring <img> tags at the moment: {element}")
doc.add_picture(
parent=self.parents[self.level],
caption=None,
content_layer=self.content_layer,
)

159
docling/backend/md_backend.py
View File

@ -24,14 +24,19 @@ from docling_core.types.doc import (
from marko import Markdown
from docling.backend.abstract_backend import DeclarativeDocumentBackend
from docling.backend.html_backend import HTMLDocumentBackend
from docling.datamodel.base_models import InputFormat
from docling.datamodel.document import InputDocument
_log = logging.getLogger(__name__)
_MARKER_BODY = "DOCLING_DOC_MD_HTML_EXPORT"
_START_MARKER = f"#_#_{_MARKER_BODY}_START_#_#"
_STOP_MARKER = f"#_#_{_MARKER_BODY}_STOP_#_#"
class MarkdownDocumentBackend(DeclarativeDocumentBackend):
def shorten_underscore_sequences(self, markdown_text: str, max_length: int = 10):
def _shorten_underscore_sequences(self, markdown_text: str, max_length: int = 10):
# This regex will match any sequence of underscores
pattern = r"_+"
@ -67,6 +72,7 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
self.in_table = False
self.md_table_buffer: list[str] = []
self.inline_texts: list[str] = []
self._html_blocks: int = 0
try:
if isinstance(self.path_or_stream, BytesIO):
@ -75,7 +81,7 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
# very long sequences of underscores will lead to unnecessary long processing times.
# In any proper Markdown files, underscores have to be escaped,
# otherwise they represent emphasis (bold or italic)
self.markdown = self.shorten_underscore_sequences(text_stream)
self.markdown = self._shorten_underscore_sequences(text_stream)
if isinstance(self.path_or_stream, Path):
with open(self.path_or_stream, "r", encoding="utf-8") as f:
md_content = f.read()
@ -83,7 +89,7 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
# very long sequences of underscores will lead to unnecessary long processing times.
# In any proper Markdown files, underscores have to be escaped,
# otherwise they represent emphasis (bold or italic)
self.markdown = self.shorten_underscore_sequences(md_content)
self.markdown = self._shorten_underscore_sequences(md_content)
self.valid = True
_log.debug(self.markdown)
@ -93,7 +99,7 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
) from e
return
def close_table(self, doc: DoclingDocument):
def _close_table(self, doc: DoclingDocument):
if self.in_table:
_log.debug("=== TABLE START ===")
for md_table_row in self.md_table_buffer:
@ -150,30 +156,35 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
doc.add_table(data=table_data)
return
def process_inline_text(
self, parent_element: Optional[NodeItem], doc: DoclingDocument
def _process_inline_text(
self, parent_item: Optional[NodeItem], doc: DoclingDocument
):
txt = " ".join(self.inline_texts)
if len(txt) > 0:
doc.add_text(
label=DocItemLabel.PARAGRAPH,
parent=parent_element,
parent=parent_item,
text=txt,
)
self.inline_texts = []
def iterate_elements(
def _iterate_elements(
self,
element: marko.element.Element,
depth: int,
doc: DoclingDocument,
parent_element: Optional[NodeItem] = None,
visited: Set[marko.element.Element],
parent_item: Optional[NodeItem] = None,
):
if element in visited:
return
# Iterates over all elements in the AST
# Check for different element types and process relevant details
if isinstance(element, marko.block.Heading) and len(element.children) > 0:
self.close_table(doc)
self.process_inline_text(parent_element, doc)
self._close_table(doc)
self._process_inline_text(parent_item, doc)
_log.debug(
f" - Heading level {element.level}, content: {element.children[0].children}" # type: ignore
)
@ -201,8 +212,8 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
traverse(element)
snippet_text = "".join(strings)
if len(snippet_text) > 0:
parent_element = doc.add_text(
label=doc_label, parent=parent_element, text=snippet_text
parent_item = doc.add_text(
label=doc_label, parent=parent_item, text=snippet_text
)
elif isinstance(element, marko.block.List):
@ -212,35 +223,37 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
has_non_empty_list_items = True
break
self.close_table(doc)
self.process_inline_text(parent_element, doc)
self._close_table(doc)
self._process_inline_text(parent_item, doc)
_log.debug(f" - List {'ordered' if element.ordered else 'unordered'}")
if has_non_empty_list_items:
label = GroupLabel.ORDERED_LIST if element.ordered else GroupLabel.LIST
parent_element = doc.add_group(
label=label, name=f"list", parent=parent_element
parent_item = doc.add_group(
label=label, name=f"list", parent=parent_item
)
elif isinstance(element, marko.block.ListItem) and len(element.children) > 0:
self.close_table(doc)
self.process_inline_text(parent_element, doc)
self._close_table(doc)
self._process_inline_text(parent_item, doc)
_log.debug(" - List item")
snippet_text = str(element.children[0].children[0].children) # type: ignore
first_child = element.children[0]
snippet_text = str(first_child.children[0].children) # type: ignore
is_numbered = False
if (
parent_element is not None
and isinstance(parent_element, DocItem)
and parent_element.label == GroupLabel.ORDERED_LIST
parent_item is not None
and isinstance(parent_item, DocItem)
and parent_item.label == GroupLabel.ORDERED_LIST
):
is_numbered = True
doc.add_list_item(
enumerated=is_numbered, parent=parent_element, text=snippet_text
enumerated=is_numbered, parent=parent_item, text=snippet_text
)
visited.add(first_child)
elif isinstance(element, marko.inline.Image):
self.close_table(doc)
self.process_inline_text(parent_element, doc)
self._close_table(doc)
self._process_inline_text(parent_item, doc)
_log.debug(f" - Image with alt: {element.title}, url: {element.dest}")
fig_caption: Optional[TextItem] = None
@ -249,10 +262,10 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
label=DocItemLabel.CAPTION, text=element.title
)
doc.add_picture(parent=parent_element, caption=fig_caption)
doc.add_picture(parent=parent_item, caption=fig_caption)
elif isinstance(element, marko.block.Paragraph) and len(element.children) > 0:
self.process_inline_text(parent_element, doc)
self._process_inline_text(parent_item, doc)
elif isinstance(element, marko.inline.RawText):
_log.debug(f" - Paragraph (raw text): {element.children}")
@ -266,17 +279,16 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
else:
self.md_table_buffer.append(snippet_text)
else:
self.close_table(doc)
self.in_table = False
self._close_table(doc)
# most likely just inline text
self.inline_texts.append(str(element.children))
elif isinstance(element, marko.inline.CodeSpan):
self.close_table(doc)
self.process_inline_text(parent_element, doc)
self._close_table(doc)
self._process_inline_text(parent_item, doc)
_log.debug(f" - Code Span: {element.children}")
snippet_text = str(element.children).strip()
doc.add_code(parent=parent_element, text=snippet_text)
doc.add_code(parent=parent_item, text=snippet_text)
elif (
isinstance(element, (marko.block.CodeBlock, marko.block.FencedCode))
@ -284,10 +296,10 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
and isinstance((first_child := element.children[0]), marko.inline.RawText)
and len(snippet_text := (first_child.children.strip())) > 0
):
self.close_table(doc)
self.process_inline_text(parent_element, doc)
self._close_table(doc)
self._process_inline_text(parent_item, doc)
_log.debug(f" - Code Block: {element.children}")
doc.add_code(parent=parent_element, text=snippet_text)
doc.add_code(parent=parent_item, text=snippet_text)
elif isinstance(element, marko.inline.LineBreak):
if self.in_table:
@ -295,27 +307,27 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
self.md_table_buffer.append("")
elif isinstance(element, marko.block.HTMLBlock):
self.process_inline_text(parent_element, doc)
self.close_table(doc)
self._html_blocks += 1
self._process_inline_text(parent_item, doc)
self._close_table(doc)
_log.debug("HTML Block: {}".format(element))
if (
len(element.children) > 0
len(element.body) > 0
): # If Marko doesn't return any content for HTML block, skip it
snippet_text = str(element.children).strip()
doc.add_text(
label=DocItemLabel.CODE, parent=parent_element, text=snippet_text
)
html_block = element.body.strip()
# wrap in markers to enable post-processing in convert()
text_to_add = f"{_START_MARKER}{html_block}{_STOP_MARKER}"
doc.add_code(parent=parent_item, text=text_to_add)
else:
if not isinstance(element, str):
self.close_table(doc)
self._close_table(doc)
_log.debug("Some other element: {}".format(element))
processed_block_types = (
marko.block.ListItem,
marko.block.Heading,
marko.block.CodeBlock,
marko.block.FencedCode,
# marko.block.Paragraph,
marko.inline.RawText,
)
@ -324,7 +336,13 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
element, processed_block_types
):
for child in element.children:
self.iterate_elements(child, depth + 1, doc, parent_element)
self._iterate_elements(
element=child,
depth=depth + 1,
doc=doc,
visited=visited,
parent_item=parent_item,
)
def is_valid(self) -> bool:
return self.valid
@ -358,8 +376,51 @@ class MarkdownDocumentBackend(DeclarativeDocumentBackend):
marko_parser = Markdown()
parsed_ast = marko_parser.parse(self.markdown)
# Start iterating from the root of the AST
self.iterate_elements(parsed_ast, 0, doc, None)
self.process_inline_text(None, doc) # handle last hanging inline text
self._iterate_elements(
element=parsed_ast,
depth=0,
doc=doc,
parent_item=None,
visited=set(),
)
self._process_inline_text(None, doc) # handle last hanging inline text
self._close_table(doc=doc) # handle any last hanging table
# if HTML blocks were detected, export to HTML and delegate to HTML backend
if self._html_blocks > 0:
# export to HTML
html_backend_cls = HTMLDocumentBackend
html_str = doc.export_to_html()
def _restore_original_html(txt, regex):
_txt, count = re.subn(regex, "", txt)
if count != self._html_blocks:
raise RuntimeError(
"An internal error has occurred during Markdown conversion."
)
return _txt
# restore original HTML by removing previouly added markers
for regex in [
rf"<pre>\s*<code>\s*{_START_MARKER}",
rf"{_STOP_MARKER}\s*</code>\s*</pre>",
]:
html_str = _restore_original_html(txt=html_str, regex=regex)
self._html_blocks = 0
# delegate to HTML backend
stream = BytesIO(bytes(html_str, encoding="utf-8"))
in_doc = InputDocument(
path_or_stream=stream,
format=InputFormat.HTML,
backend=html_backend_cls,
filename=self.file.name,
)
html_backend_obj = html_backend_cls(
in_doc=in_doc, path_or_stream=stream
)
doc = html_backend_obj.convert()
else:
raise RuntimeError(
f"Cannot convert md with {self.document_hash} because the backend failed to init."

57
docling/backend/mspowerpoint_backend.py
View File

@ -98,21 +98,28 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB
return doc
def generate_prov(self, shape, slide_ind, text=""):
left = shape.left
top = shape.top
width = shape.width
height = shape.height
def generate_prov(
self, shape, slide_ind, text="", slide_size=Size(width=1, height=1)
):
if shape.left:
left = shape.left
top = shape.top
width = shape.width
height = shape.height
else:
left = 0
top = 0
width = slide_size.width
height = slide_size.height
shape_bbox = [left, top, left + width, top + height]
shape_bbox = BoundingBox.from_tuple(shape_bbox, origin=CoordOrigin.BOTTOMLEFT)
# prov = [{"bbox": shape_bbox, "page": parent_slide, "span": [0, len(text)]}]
prov = ProvenanceItem(
page_no=slide_ind + 1, charspan=[0, len(text)], bbox=shape_bbox
)
return prov
def handle_text_elements(self, shape, parent_slide, slide_ind, doc):
def handle_text_elements(self, shape, parent_slide, slide_ind, doc, slide_size):
is_a_list = False
is_list_group_created = False
enum_list_item_value = 0
@ -121,7 +128,7 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB
list_text = ""
list_label = GroupLabel.LIST
doc_label = DocItemLabel.LIST_ITEM
prov = self.generate_prov(shape, slide_ind, shape.text.strip())
prov = self.generate_prov(shape, slide_ind, shape.text.strip(), slide_size)
# Identify if shape contains lists
for paragraph in shape.text_frame.paragraphs:
@ -270,7 +277,7 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB
)
return
def handle_pictures(self, shape, parent_slide, slide_ind, doc):
def handle_pictures(self, shape, parent_slide, slide_ind, doc, slide_size):
# Open it with PIL
try:
# Get the image bytes
@ -280,7 +287,7 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB
pil_image = Image.open(BytesIO(image_bytes))
# shape has picture
prov = self.generate_prov(shape, slide_ind, "")
prov = self.generate_prov(shape, slide_ind, "", slide_size)
doc.add_picture(
parent=parent_slide,
image=ImageRef.from_pil(image=pil_image, dpi=im_dpi),
@ -291,13 +298,13 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB
_log.warning(f"Warning: image cannot be loaded by Pillow: {e}")
return
def handle_tables(self, shape, parent_slide, slide_ind, doc):
def handle_tables(self, shape, parent_slide, slide_ind, doc, slide_size):
# Handling tables, images, charts
if shape.has_table:
table = shape.table
table_xml = shape._element
prov = self.generate_prov(shape, slide_ind, "")
prov = self.generate_prov(shape, slide_ind, "", slide_size)
num_cols = 0
num_rows = len(table.rows)
@ -374,17 +381,19 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB
name=f"slide-{slide_ind}", label=GroupLabel.CHAPTER, parent=parents[0]
)
size = Size(width=slide_width, height=slide_height)
parent_page = doc.add_page(page_no=slide_ind + 1, size=size)
slide_size = Size(width=slide_width, height=slide_height)
parent_page = doc.add_page(page_no=slide_ind + 1, size=slide_size)
def handle_shapes(shape, parent_slide, slide_ind, doc):
handle_groups(shape, parent_slide, slide_ind, doc)
def handle_shapes(shape, parent_slide, slide_ind, doc, slide_size):
handle_groups(shape, parent_slide, slide_ind, doc, slide_size)
if shape.has_table:
# Handle Tables
self.handle_tables(shape, parent_slide, slide_ind, doc)
self.handle_tables(shape, parent_slide, slide_ind, doc, slide_size)
if shape.shape_type == MSO_SHAPE_TYPE.PICTURE:
# Handle Pictures
self.handle_pictures(shape, parent_slide, slide_ind, doc)
self.handle_pictures(
shape, parent_slide, slide_ind, doc, slide_size
)
# If shape doesn't have any text, move on to the next shape
if not hasattr(shape, "text"):
return
@ -396,16 +405,20 @@ class MsPowerpointDocumentBackend(DeclarativeDocumentBackend, PaginatedDocumentB
_log.warning("Warning: shape has text but not text_frame")
return
# Handle other text elements, including lists (bullet lists, numbered lists)
self.handle_text_elements(shape, parent_slide, slide_ind, doc)
self.handle_text_elements(
shape, parent_slide, slide_ind, doc, slide_size
)
return
def handle_groups(shape, parent_slide, slide_ind, doc):
def handle_groups(shape, parent_slide, slide_ind, doc, slide_size):
if shape.shape_type == MSO_SHAPE_TYPE.GROUP:
for groupedshape in shape.shapes:
handle_shapes(groupedshape, parent_slide, slide_ind, doc)
handle_shapes(
groupedshape, parent_slide, slide_ind, doc, slide_size
)
# Loop through each shape in the slide
for shape in slide.shapes:
handle_shapes(shape, parent_slide, slide_ind, doc)
handle_shapes(shape, parent_slide, slide_ind, doc, slide_size)
return doc

287
docling/backend/msword_backend.py
View File

@ -2,21 +2,28 @@ import logging
import re
from io import BytesIO
from pathlib import Path
from typing import Set, Union
from typing import Any, Optional, Union
import docx
from docling_core.types.doc import (
DocItemLabel,
DoclingDocument,
DocumentOrigin,
GroupLabel,
ImageRef,
NodeItem,
TableCell,
TableData,
)
from docx import Document
from docx.document import Document as DocxDocument
from docx.oxml.table import CT_Tc
from docx.oxml.xmlchemy import BaseOxmlElement
from docx.table import Table, _Cell
from docx.text.paragraph import Paragraph
from lxml import etree
from lxml.etree import XPath
from PIL import Image, UnidentifiedImageError
from typing_extensions import override
from docling.backend.abstract_backend import DeclarativeDocumentBackend
from docling.backend.docx_latex.omml import oMath2Latex
@ -27,7 +34,10 @@ _log = logging.getLogger(__name__)
class MsWordDocumentBackend(DeclarativeDocumentBackend):
def __init__(self, in_doc: "InputDocument", path_or_stream: Union[BytesIO, Path]):
@override
def __init__(
self, in_doc: "InputDocument", path_or_stream: Union[BytesIO, Path]
) -> None:
super().__init__(in_doc, path_or_stream)
self.XML_KEY = (
"{http://schemas.openxmlformats.org/wordprocessingml/2006/main}val"
@ -37,19 +47,19 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
}
# self.initialise(path_or_stream)
# Word file:
self.path_or_stream = path_or_stream
self.valid = False
self.path_or_stream: Union[BytesIO, Path] = path_or_stream
self.valid: bool = False
# Initialise the parents for the hierarchy
self.max_levels = 10
self.level_at_new_list = None
self.parents = {} # type: ignore
self.max_levels: int = 10
self.level_at_new_list: Optional[int] = None
self.parents: dict[int, Optional[NodeItem]] = {}
for i in range(-1, self.max_levels):
self.parents[i] = None
self.level = 0
self.listIter = 0
self.history = {
self.history: dict[str, Any] = {
"names": [None],
"levels": [None],
"numids": [None],
@ -59,9 +69,9 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
self.docx_obj = None
try:
if isinstance(self.path_or_stream, BytesIO):
self.docx_obj = docx.Document(self.path_or_stream)
self.docx_obj = Document(self.path_or_stream)
elif isinstance(self.path_or_stream, Path):
self.docx_obj = docx.Document(str(self.path_or_stream))
self.docx_obj = Document(str(self.path_or_stream))
self.valid = True
except Exception as e:
@ -69,13 +79,16 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
f"MsPowerpointDocumentBackend could not load document with hash {self.document_hash}"
) from e
@override
def is_valid(self) -> bool:
return self.valid
@classmethod
@override
def supports_pagination(cls) -> bool:
return False
@override
def unload(self):
if isinstance(self.path_or_stream, BytesIO):
self.path_or_stream.close()
@ -83,11 +96,17 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
self.path_or_stream = None
@classmethod
def supported_formats(cls) -> Set[InputFormat]:
@override
def supported_formats(cls) -> set[InputFormat]:
return {InputFormat.DOCX}
@override
def convert(self) -> DoclingDocument:
# Parses the DOCX into a structured document model.
"""Parses the DOCX into a structured document model.
Returns:
The parsed document.
"""
origin = DocumentOrigin(
filename=self.file.name or "file",
@ -105,23 +124,29 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
f"Cannot convert doc with {self.document_hash} because the backend failed to init."
)
def update_history(self, name, level, numid, ilevel):
def update_history(
self,
name: str,
level: Optional[int],
numid: Optional[int],
ilevel: Optional[int],
):
self.history["names"].append(name)
self.history["levels"].append(level)
self.history["numids"].append(numid)
self.history["indents"].append(ilevel)
def prev_name(self):
def prev_name(self) -> Optional[str]:
return self.history["names"][-1]
def prev_level(self):
def prev_level(self) -> Optional[int]:
return self.history["levels"][-1]
def prev_numid(self):
def prev_numid(self) -> Optional[int]:
return self.history["numids"][-1]
def prev_indent(self):
def prev_indent(self) -> Optional[int]:
return self.history["indents"][-1]
def get_level(self) -> int:
@ -131,7 +156,12 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
return k
return 0
def walk_linear(self, body, docx_obj, doc) -> DoclingDocument:
def walk_linear(
self,
body: BaseOxmlElement,
docx_obj: DocxDocument,
doc: DoclingDocument,
) -> DoclingDocument:
for element in body:
tag_name = etree.QName(element).localname
@ -139,6 +169,7 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
namespaces = {
"a": "http://schemas.openxmlformats.org/drawingml/2006/main",
"r": "http://schemas.openxmlformats.org/officeDocument/2006/relationships",
"w": "http://schemas.openxmlformats.org/wordprocessingml/2006/main",
}
xpath_expr = XPath(".//a:blip", namespaces=namespaces)
drawing_blip = xpath_expr(element)
@ -151,7 +182,15 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
_log.debug("could not parse a table, broken docx table")
elif drawing_blip:
self.handle_pictures(element, docx_obj, drawing_blip, doc)
self.handle_pictures(docx_obj, drawing_blip, doc)
# Check for the sdt containers, like table of contents
elif tag_name in ["sdt"]:
sdt_content = element.find(".//w:sdtContent", namespaces=namespaces)
if sdt_content is not None:
# Iterate paragraphs, runs, or text inside <w:sdtContent>.
paragraphs = sdt_content.findall(".//w:p", namespaces=namespaces)
for p in paragraphs:
self.handle_text_elements(p, docx_obj, doc)
# Check for Text
elif tag_name in ["p"]:
# "tcPr", "sectPr"
@ -160,7 +199,7 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
_log.debug(f"Ignoring element in DOCX with tag: {tag_name}")
return doc
def str_to_int(self, s, default=0):
def str_to_int(self, s: Optional[str], default: Optional[int] = 0) -> Optional[int]:
if s is None:
return None
try:
@ -168,7 +207,7 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
except ValueError:
return default
def split_text_and_number(self, input_string):
def split_text_and_number(self, input_string: str) -> list[str]:
match = re.match(r"(\D+)(\d+)$|^(\d+)(\D+)", input_string)
if match:
parts = list(filter(None, match.groups()))
@ -176,7 +215,9 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
else:
return [input_string]
def get_numId_and_ilvl(self, paragraph):
def get_numId_and_ilvl(
self, paragraph: Paragraph
) -> tuple[Optional[int], Optional[int]]:
# Access the XML element of the paragraph
numPr = paragraph._element.find(
".//w:numPr", namespaces=paragraph._element.nsmap
@ -189,13 +230,11 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
numId = numId_elem.get(self.XML_KEY) if numId_elem is not None else None
ilvl = ilvl_elem.get(self.XML_KEY) if ilvl_elem is not None else None
return self.str_to_int(numId, default=None), self.str_to_int(
ilvl, default=None
)
return self.str_to_int(numId, None), self.str_to_int(ilvl, None)
return None, None # If the paragraph is not part of a list
def get_label_and_level(self, paragraph):
def get_label_and_level(self, paragraph: Paragraph) -> tuple[str, Optional[int]]:
if paragraph.style is None:
return "Normal", None
label = paragraph.style.style_id
@ -205,42 +244,32 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
parts = label.split(":")
if len(parts) == 2:
return parts[0], int(parts[1])
return parts[0], self.str_to_int(parts[1], None)
parts = self.split_text_and_number(label)
if "Heading" in label and len(parts) == 2:
parts.sort()
label_str = ""
label_level = 0
label_str: str = ""
label_level: Optional[int] = 0
if parts[0] == "Heading":
label_str = parts[0]
label_level = self.str_to_int(parts[1], default=None)
label_level = self.str_to_int(parts[1], None)
if parts[1] == "Heading":
label_str = parts[1]
label_level = self.str_to_int(parts[0], default=None)
label_level = self.str_to_int(parts[0], None)
return label_str, label_level
else:
return label, None
def handle_equations_in_text(self, element, text):
only_texts = []
texts_and_equations = []
for subt in element.iter():
tag_name = etree.QName(subt).localname
if tag_name == "t" and "math" not in subt.tag:
only_texts.append(subt.text)
texts_and_equations.append(subt.text)
elif "oMath" in subt.tag and "oMathPara" not in subt.tag:
texts_and_equations.append(f"${str(oMath2Latex(subt))}$")
if "".join(only_texts) != text:
return text
return "".join(texts_and_equations)
def handle_text_elements(self, element, docx_obj, doc):
paragraph = docx.text.paragraph.Paragraph(element, docx_obj)
def handle_text_elements(
self,
element: BaseOxmlElement,
docx_obj: DocxDocument,
doc: DoclingDocument,
) -> None:
paragraph = Paragraph(element, docx_obj)
raw_text = paragraph.text
text = self.handle_equations_in_text(element=element, text=raw_text)
@ -267,11 +296,7 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
and p_style_id not in ["Title", "Heading"]
):
self.add_listitem(
element,
docx_obj,
doc,
p_style_id,
p_level,
numid,
ilevel,
text,
@ -296,14 +321,14 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
self.level = 0
if p_style_id in ["Title"]:
for key, val in self.parents.items():
for key in range(len(self.parents)):
self.parents[key] = None
self.parents[0] = doc.add_text(
parent=None, label=DocItemLabel.TITLE, text=text
)
elif "Heading" in p_style_id:
self.add_header(element, docx_obj, doc, p_style_id, p_level, text)
self.add_header(doc, p_level, text)
elif p_style_id in [
"Subtitle",
@ -347,7 +372,9 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
self.update_history(p_style_id, p_level, numid, ilevel)
return
def add_header(self, element, docx_obj, doc, curr_name, curr_level, text: str):
def add_header(
self, doc: DoclingDocument, curr_level: Optional[int], text: str
) -> None:
level = self.get_level()
if isinstance(curr_level, int):
if curr_level > level:
@ -360,7 +387,7 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
)
elif curr_level < level:
# remove the tail
for key, val in self.parents.items():
for key in range(len(self.parents)):
if key >= curr_level:
self.parents[key] = None
@ -379,22 +406,18 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
def add_listitem(
self,
element,
docx_obj,
doc,
p_style_id,
p_level,
numid,
ilevel,
doc: DoclingDocument,
numid: int,
ilevel: int,
text: str,
is_numbered=False,
):
# is_numbered = is_numbered
is_numbered: bool = False,
) -> None:
enum_marker = ""
level = self.get_level()
prev_indent = self.prev_indent()
if self.prev_numid() is None: # Open new list
self.level_at_new_list = level # type: ignore
self.level_at_new_list = level
self.parents[level] = doc.add_group(
label=GroupLabel.LIST, name="list", parent=self.parents[level - 1]
@ -413,10 +436,13 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
)
elif (
self.prev_numid() == numid and self.prev_indent() < ilevel
self.prev_numid() == numid
and self.level_at_new_list is not None
and prev_indent is not None
and prev_indent < ilevel
): # Open indented list
for i in range(
self.level_at_new_list + self.prev_indent() + 1,
self.level_at_new_list + prev_indent + 1,
self.level_at_new_list + ilevel + 1,
):
# Determine if this is an unordered list or an ordered list.
@ -445,7 +471,12 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
text=text,
)
elif self.prev_numid() == numid and ilevel < self.prev_indent(): # Close list
elif (
self.prev_numid() == numid
and self.level_at_new_list is not None
and prev_indent is not None
and ilevel < prev_indent
): # Close list
for k, v in self.parents.items():
if k > self.level_at_new_list + ilevel:
self.parents[k] = None
@ -463,7 +494,7 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
)
self.listIter = 0
elif self.prev_numid() == numid or self.prev_indent() == ilevel:
elif self.prev_numid() == numid or prev_indent == ilevel:
# TODO: Set marker and enumerated arguments if this is an enumeration element.
self.listIter += 1
if is_numbered:
@ -477,31 +508,16 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
)
return
def handle_tables(self, element, docx_obj, doc):
# Function to check if a cell has a colspan (gridSpan)
def get_colspan(cell):
grid_span = cell._element.xpath("@w:gridSpan")
if grid_span:
return int(grid_span[0]) # Return the number of columns spanned
return 1 # Default is 1 (no colspan)
# Function to check if a cell has a rowspan (vMerge)
def get_rowspan(cell):
v_merge = cell._element.xpath("@w:vMerge")
if v_merge:
return v_merge[
0
] # 'restart' indicates the beginning of a rowspan, others are continuation
return 1
table = docx.table.Table(element, docx_obj)
def handle_tables(
self,
element: BaseOxmlElement,
docx_obj: DocxDocument,
doc: DoclingDocument,
) -> None:
table: Table = Table(element, docx_obj)
num_rows = len(table.rows)
num_cols = 0
for row in table.rows:
# Calculate the max number of columns
num_cols = max(num_cols, sum(get_colspan(cell) for cell in row.cells))
num_cols = len(table.columns)
_log.debug(f"Table grid with {num_rows} rows and {num_cols} columns")
if num_rows == 1 and num_cols == 1:
cell_element = table.rows[0].cells[0]
@ -510,59 +526,56 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
self.walk_linear(cell_element._element, docx_obj, doc)
return
# Initialize the table grid
table_grid = [[None for _ in range(num_cols)] for _ in range(num_rows)]
data = TableData(num_rows=num_rows, num_cols=num_cols, table_cells=[])
data = TableData(num_rows=num_rows, num_cols=num_cols)
cell_set: set[CT_Tc] = set()
for row_idx, row in enumerate(table.rows):
_log.debug(f"Row index {row_idx} with {len(row.cells)} populated cells")
col_idx = 0
for c, cell in enumerate(row.cells):
row_span = get_rowspan(cell)
col_span = get_colspan(cell)
while col_idx < num_cols:
cell: _Cell = row.cells[col_idx]
_log.debug(
f" col {col_idx} grid_span {cell.grid_span} grid_cols_before {row.grid_cols_before}"
)
if cell is None or cell._tc in cell_set:
_log.debug(f" skipped since repeated content")
col_idx += cell.grid_span
continue
else:
cell_set.add(cell._tc)
cell_text = cell.text
# In case cell doesn't return text via docx library:
if len(cell_text) == 0:
cell_xml = cell._element
spanned_idx = row_idx
spanned_tc: Optional[CT_Tc] = cell._tc
while spanned_tc == cell._tc:
spanned_idx += 1
spanned_tc = (
table.rows[spanned_idx].cells[col_idx]._tc
if spanned_idx < num_rows
else None
)
_log.debug(f" spanned before row {spanned_idx}")
texts = [""]
for elem in cell_xml.iter():
if elem.tag.endswith("t"): # <w:t> tags that contain text
if elem.text:
texts.append(elem.text)
# Join the collected text
cell_text = " ".join(texts).strip()
# Find the next available column in the grid
while table_grid[row_idx][col_idx] is not None:
col_idx += 1
# Fill the grid with the cell value, considering rowspan and colspan
for i in range(row_span if row_span == "restart" else 1):
for j in range(col_span):
table_grid[row_idx + i][col_idx + j] = ""
cell = TableCell(
text=cell_text,
row_span=row_span,
col_span=col_span,
start_row_offset_idx=row_idx,
end_row_offset_idx=row_idx + row_span,
table_cell = TableCell(
text=cell.text,
row_span=spanned_idx - row_idx,
col_span=cell.grid_span,
start_row_offset_idx=row.grid_cols_before + row_idx,
end_row_offset_idx=row.grid_cols_before + spanned_idx,
start_col_offset_idx=col_idx,
end_col_offset_idx=col_idx + col_span,
end_col_offset_idx=col_idx + cell.grid_span,
col_header=False,
row_header=False,
)
data.table_cells.append(cell)
data.table_cells.append(table_cell)
col_idx += cell.grid_span
level = self.get_level()
doc.add_table(data=data, parent=self.parents[level - 1])
return
def handle_pictures(self, element, docx_obj, drawing_blip, doc):
def get_docx_image(element, drawing_blip):
def handle_pictures(
self, docx_obj: DocxDocument, drawing_blip: Any, doc: DoclingDocument
) -> None:
def get_docx_image(drawing_blip):
rId = drawing_blip[0].get(
"{http://schemas.openxmlformats.org/officeDocument/2006/relationships}embed"
)
@ -575,7 +588,7 @@ class MsWordDocumentBackend(DeclarativeDocumentBackend):
level = self.get_level()
# Open the BytesIO object with PIL to create an Image
try:
image_data = get_docx_image(element, drawing_blip)
image_data = get_docx_image(drawing_blip)
image_bytes = BytesIO(image_data)
pil_image = Image.open(image_bytes)
doc.add_picture(

98
docling/backend/pypdfium2_backend.py
View File

@ -13,6 +13,7 @@ from pypdfium2._helpers.misc import PdfiumError
from docling.backend.pdf_backend import PdfDocumentBackend, PdfPageBackend
from docling.datamodel.base_models import Cell
from docling.utils.locks import pypdfium2_lock
if TYPE_CHECKING:
from docling.datamodel.document import InputDocument
@ -24,6 +25,7 @@ class PyPdfiumPageBackend(PdfPageBackend):
def __init__(
self, pdfium_doc: pdfium.PdfDocument, document_hash: str, page_no: int
):
# Note: lock applied by the caller
self.valid = True # No better way to tell from pypdfium.
try:
self._ppage: pdfium.PdfPage = pdfium_doc[page_no]
@ -40,51 +42,57 @@ class PyPdfiumPageBackend(PdfPageBackend):
def get_bitmap_rects(self, scale: float = 1) -> Iterable[BoundingBox]:
AREA_THRESHOLD = 0 # 32 * 32
for obj in self._ppage.get_objects(filter=[pdfium_c.FPDF_PAGEOBJ_IMAGE]):
pos = obj.get_pos()
cropbox = BoundingBox.from_tuple(
pos, origin=CoordOrigin.BOTTOMLEFT
).to_top_left_origin(page_height=self.get_size().height)
page_size = self.get_size()
with pypdfium2_lock:
for obj in self._ppage.get_objects(filter=[pdfium_c.FPDF_PAGEOBJ_IMAGE]):
pos = obj.get_pos()
cropbox = BoundingBox.from_tuple(
pos, origin=CoordOrigin.BOTTOMLEFT
).to_top_left_origin(page_height=page_size.height)
if cropbox.area() > AREA_THRESHOLD:
cropbox = cropbox.scaled(scale=scale)
if cropbox.area() > AREA_THRESHOLD:
cropbox = cropbox.scaled(scale=scale)
yield cropbox
yield cropbox
def get_text_in_rect(self, bbox: BoundingBox) -> str:
if not self.text_page:
self.text_page = self._ppage.get_textpage()
with pypdfium2_lock:
if not self.text_page:
self.text_page = self._ppage.get_textpage()
if bbox.coord_origin != CoordOrigin.BOTTOMLEFT:
bbox = bbox.to_bottom_left_origin(self.get_size().height)
text_piece = self.text_page.get_text_bounded(*bbox.as_tuple())
with pypdfium2_lock:
text_piece = self.text_page.get_text_bounded(*bbox.as_tuple())
return text_piece
def get_text_cells(self) -> Iterable[Cell]:
if not self.text_page:
self.text_page = self._ppage.get_textpage()
with pypdfium2_lock:
if not self.text_page:
self.text_page = self._ppage.get_textpage()
cells = []
cell_counter = 0
page_size = self.get_size()
for i in range(self.text_page.count_rects()):
rect = self.text_page.get_rect(i)
text_piece = self.text_page.get_text_bounded(*rect)
x0, y0, x1, y1 = rect
cells.append(
Cell(
id=cell_counter,
text=text_piece,
bbox=BoundingBox(
l=x0, b=y0, r=x1, t=y1, coord_origin=CoordOrigin.BOTTOMLEFT
).to_top_left_origin(page_size.height),
with pypdfium2_lock:
for i in range(self.text_page.count_rects()):
rect = self.text_page.get_rect(i)
text_piece = self.text_page.get_text_bounded(*rect)
x0, y0, x1, y1 = rect
cells.append(
Cell(
id=cell_counter,
text=text_piece,
bbox=BoundingBox(
l=x0, b=y0, r=x1, t=y1, coord_origin=CoordOrigin.BOTTOMLEFT
).to_top_left_origin(page_size.height),
)
)
)
cell_counter += 1
cell_counter += 1
# PyPdfium2 produces very fragmented cells, with sub-word level boundaries, in many PDFs.
# The cell merging code below is to clean this up.
@ -214,20 +222,24 @@ class PyPdfiumPageBackend(PdfPageBackend):
padbox.r = page_size.width - padbox.r
padbox.t = page_size.height - padbox.t
image = (
self._ppage.render(
scale=scale * 1.5,
rotation=0, # no additional rotation
crop=padbox.as_tuple(),
)
.to_pil()
.resize(size=(round(cropbox.width * scale), round(cropbox.height * scale)))
) # We resize the image from 1.5x the given scale to make it sharper.
with pypdfium2_lock:
image = (
self._ppage.render(
scale=scale * 1.5,
rotation=0, # no additional rotation
crop=padbox.as_tuple(),
)
.to_pil()
.resize(
size=(round(cropbox.width * scale), round(cropbox.height * scale))
)
) # We resize the image from 1.5x the given scale to make it sharper.
return image
def get_size(self) -> Size:
return Size(width=self._ppage.get_width(), height=self._ppage.get_height())
with pypdfium2_lock:
return Size(width=self._ppage.get_width(), height=self._ppage.get_height())
def unload(self):
self._ppage = None
@ -239,22 +251,26 @@ class PyPdfiumDocumentBackend(PdfDocumentBackend):
super().__init__(in_doc, path_or_stream)
try:
self._pdoc = pdfium.PdfDocument(self.path_or_stream)
with pypdfium2_lock:
self._pdoc = pdfium.PdfDocument(self.path_or_stream)
except PdfiumError as e:
raise RuntimeError(
f"pypdfium could not load document with hash {self.document_hash}"
) from e
def page_count(self) -> int:
return len(self._pdoc)
with pypdfium2_lock:
return len(self._pdoc)
def load_page(self, page_no: int) -> PyPdfiumPageBackend:
return PyPdfiumPageBackend(self._pdoc, self.document_hash, page_no)
with pypdfium2_lock:
return PyPdfiumPageBackend(self._pdoc, self.document_hash, page_no)
def is_valid(self) -> bool:
return self.page_count() > 0
def unload(self):
super().unload()
self._pdoc.close()
self._pdoc = None
with pypdfium2_lock:
self._pdoc.close()
self._pdoc = None

710
docling/backend/xml/jats_backend.py Executable file
View File

@ -0,0 +1,710 @@
import logging
import traceback
from io import BytesIO
from pathlib import Path
from typing import Final, Optional, Union
from bs4 import BeautifulSoup, Tag
from docling_core.types.doc import (
DocItemLabel,
DoclingDocument,
DocumentOrigin,
GroupItem,
GroupLabel,
NodeItem,
TextItem,
)
from lxml import etree
from typing_extensions import TypedDict, override
from docling.backend.abstract_backend import DeclarativeDocumentBackend
from docling.backend.html_backend import HTMLDocumentBackend
from docling.datamodel.base_models import InputFormat
from docling.datamodel.document import InputDocument
_log = logging.getLogger(__name__)
JATS_DTD_URL: Final = ["JATS-journalpublishing", "JATS-archive"]
DEFAULT_HEADER_ACKNOWLEDGMENTS: Final = "Acknowledgments"
DEFAULT_HEADER_ABSTRACT: Final = "Abstract"
DEFAULT_HEADER_REFERENCES: Final = "References"
DEFAULT_TEXT_ETAL: Final = "et al."
class Abstract(TypedDict):
label: str
content: str
class Author(TypedDict):
name: str
affiliation_names: list[str]
class Citation(TypedDict):
author_names: str
title: str
source: str
year: str
volume: str
page: str
pub_id: str
publisher_name: str
publisher_loc: str
class Table(TypedDict):
label: str
caption: str
content: str
class XMLComponents(TypedDict):
title: str
authors: list[Author]
abstract: list[Abstract]
class JatsDocumentBackend(DeclarativeDocumentBackend):
"""Backend to parse articles in XML format tagged according to JATS definition.
The Journal Article Tag Suite (JATS) is an definition standard for the
representation of journal articles in XML format. Several publishers and journal
archives provide content in JATS format, including PubMed Central® (PMC), bioRxiv,
medRxiv, or Springer Nature.
Refer to https://jats.nlm.nih.gov for more details on JATS.
The code from this document backend has been developed by modifying parts of the
PubMed Parser library (version 0.5.0, released on 12.08.2024):
Achakulvisut et al., (2020).
Pubmed Parser: A Python Parser for PubMed Open-Access XML Subset and MEDLINE XML
Dataset XML Dataset.
Journal of Open Source Software, 5(46), 1979,
https://doi.org/10.21105/joss.01979
"""
@override
def __init__(
self, in_doc: "InputDocument", path_or_stream: Union[BytesIO, Path]
) -> None:
super().__init__(in_doc, path_or_stream)
self.path_or_stream = path_or_stream
# Initialize the root of the document hiearchy
self.root: Optional[NodeItem] = None
self.valid = False
try:
if isinstance(self.path_or_stream, BytesIO):
self.path_or_stream.seek(0)
self.tree: etree._ElementTree = etree.parse(self.path_or_stream)
doc_info: etree.DocInfo = self.tree.docinfo
if doc_info.system_url and any(
[kwd in doc_info.system_url for kwd in JATS_DTD_URL]
):
self.valid = True
return
for ent in doc_info.internalDTD.iterentities():
if ent.system_url and any(
[kwd in ent.system_url for kwd in JATS_DTD_URL]
):
self.valid = True
return
except Exception as exc:
raise RuntimeError(
f"Could not initialize JATS backend for file with hash {self.document_hash}."
) from exc
@override
def is_valid(self) -> bool:
return self.valid
@classmethod
@override
def supports_pagination(cls) -> bool:
return False
@override
def unload(self):
if isinstance(self.path_or_stream, BytesIO):
self.path_or_stream.close()
self.path_or_stream = None
@classmethod
@override
def supported_formats(cls) -> set[InputFormat]:
return {InputFormat.XML_JATS}
@override
def convert(self) -> DoclingDocument:
try:
# Create empty document
origin = DocumentOrigin(
filename=self.file.name or "file",
mimetype="application/xml",
binary_hash=self.document_hash,
)
doc = DoclingDocument(name=self.file.stem or "file", origin=origin)
# Get metadata XML components
xml_components: XMLComponents = self._parse_metadata()
# Add metadata to the document
self._add_metadata(doc, xml_components)
# walk over the XML body
body = self.tree.xpath("//body")
if self.root and len(body) > 0:
self._walk_linear(doc, self.root, body[0])
# walk over the XML back matter
back = self.tree.xpath("//back")
if self.root and len(back) > 0:
self._walk_linear(doc, self.root, back[0])
except Exception:
_log.error(traceback.format_exc())
return doc
@staticmethod
def _get_text(node: etree._Element, sep: Optional[str] = None) -> str:
skip_tags = ["term", "disp-formula", "inline-formula"]
text: str = (
node.text.replace("\n", " ")
if (node.tag not in skip_tags and node.text)
else ""
)
for child in list(node):
if child.tag not in skip_tags:
# TODO: apply styling according to child.tag when supported by docling-core
text += JatsDocumentBackend._get_text(child, sep)
if sep:
text = text.rstrip(sep) + sep
text += child.tail.replace("\n", " ") if child.tail else ""
return text
def _find_metadata(self) -> Optional[etree._Element]:
meta_names: list[str] = ["article-meta", "book-part-meta"]
meta: Optional[etree._Element] = None
for name in meta_names:
node = self.tree.xpath(f".//{name}")
if len(node) > 0:
meta = node[0]
break
return meta
def _parse_abstract(self) -> list[Abstract]:
# TODO: address cases with multiple sections
abs_list: list[Abstract] = []
for abs_node in self.tree.xpath(".//abstract"):
abstract: Abstract = dict(label="", content="")
texts = []
for abs_par in abs_node.xpath("p"):
texts.append(JatsDocumentBackend._get_text(abs_par).strip())
abstract["content"] = " ".join(texts)
label_node = abs_node.xpath("title|label")
if len(label_node) > 0:
abstract["label"] = label_node[0].text.strip()
abs_list.append(abstract)
return abs_list
def _parse_authors(self) -> list[Author]:
# Get mapping between affiliation ids and names
authors: list[Author] = []
meta: Optional[etree._Element] = self._find_metadata()
if meta is None:
return authors
affiliation_names = []
for affiliation_node in meta.xpath(".//aff[@id]"):
aff = ", ".join([t for t in affiliation_node.itertext() if t.strip()])
aff = aff.replace("\n", " ")
label = affiliation_node.xpath("label")
if label:
# TODO: once superscript is supported, add label with formatting
aff = aff.removeprefix(f"{label[0].text}, ")
affiliation_names.append(aff)
affiliation_ids_names = {
id: name
for id, name in zip(meta.xpath(".//aff[@id]/@id"), affiliation_names)
}
# Get author names and affiliation names
for author_node in meta.xpath(
'.//contrib-group/contrib[@contrib-type="author"]'
):
author: Author = {
"name": "",
"affiliation_names": [],
}
# Affiliation names
affiliation_ids = [
a.attrib["rid"] for a in author_node.xpath('xref[@ref-type="aff"]')
]
for id in affiliation_ids:
if id in affiliation_ids_names:
author["affiliation_names"].append(affiliation_ids_names[id])
# Name
author["name"] = (
author_node.xpath("name/given-names")[0].text
+ " "
+ author_node.xpath("name/surname")[0].text
)
authors.append(author)
return authors
def _parse_title(self) -> str:
meta_names: list[str] = [
"article-meta",
"collection-meta",
"book-meta",
"book-part-meta",
]
title_names: list[str] = ["article-title", "subtitle", "title", "label"]
titles: list[str] = [
" ".join(
elem.text.replace("\n", " ").strip()
for elem in list(title_node)
if elem.tag in title_names
).strip()
for title_node in self.tree.xpath(
"|".join([f".//{item}/title-group" for item in meta_names])
)
]
text = " - ".join(titles)
return text
def _parse_metadata(self) -> XMLComponents:
"""Parsing JATS document metadata."""
xml_components: XMLComponents = {
"title": self._parse_title(),
"authors": self._parse_authors(),
"abstract": self._parse_abstract(),
}
return xml_components
def _add_abstract(
self, doc: DoclingDocument, xml_components: XMLComponents
) -> None:
for abstract in xml_components["abstract"]:
text: str = abstract["content"]
title: str = abstract["label"] or DEFAULT_HEADER_ABSTRACT
if not text:
continue
parent = doc.add_heading(parent=self.root, text=title)
doc.add_text(
parent=parent,
text=text,
label=DocItemLabel.TEXT,
)
return
def _add_authors(self, doc: DoclingDocument, xml_components: XMLComponents) -> None:
# TODO: once docling supports text formatting, add affiliation reference to
# author names through superscripts
authors: list = [item["name"] for item in xml_components["authors"]]
authors_str = ", ".join(authors)
affiliations: list = [
item
for author in xml_components["authors"]
for item in author["affiliation_names"]
]
affiliations_str = "; ".join(list(dict.fromkeys(affiliations)))
if authors_str:
doc.add_text(
parent=self.root,
text=authors_str,
label=DocItemLabel.PARAGRAPH,
)
if affiliations_str:
doc.add_text(
parent=self.root,
text=affiliations_str,
label=DocItemLabel.PARAGRAPH,
)
return
def _add_citation(self, doc: DoclingDocument, parent: NodeItem, text: str) -> None:
if isinstance(parent, GroupItem) and parent.label == GroupLabel.LIST:
doc.add_list_item(text=text, enumerated=False, parent=parent)
else:
doc.add_text(text=text, label=DocItemLabel.TEXT, parent=parent)
return
def _parse_element_citation(self, node: etree._Element) -> str:
citation: Citation = {
"author_names": "",
"title": "",
"source": "",
"year": "",
"volume": "",
"page": "",
"pub_id": "",
"publisher_name": "",
"publisher_loc": "",
}
_log.debug("Citation parsing started")
# Author names
names = []
for name_node in node.xpath(".//name"):
name_str = (
name_node.xpath("surname")[0].text.replace("\n", " ").strip()
+ " "
+ name_node.xpath("given-names")[0].text.replace("\n", " ").strip()
)
names.append(name_str)
etal_node = node.xpath(".//etal")
if len(etal_node) > 0:
etal_text = etal_node[0].text or DEFAULT_TEXT_ETAL
names.append(etal_text)
citation["author_names"] = ", ".join(names)
titles: list[str] = [
"article-title",
"chapter-title",
"data-title",
"issue-title",
"part-title",
"trans-title",
]
title_node: Optional[etree._Element] = None
for name in titles:
name_node = node.xpath(name)
if len(name_node) > 0:
title_node = name_node[0]
break
citation["title"] = (
JatsDocumentBackend._get_text(title_node)
if title_node is not None
else node.text.replace("\n", " ").strip()
)
# Journal, year, publisher name, publisher location, volume, elocation
fields: list[str] = [
"source",
"year",
"publisher-name",
"publisher-loc",
"volume",
]
for item in fields:
item_node = node.xpath(item)
if len(item_node) > 0:
citation[item.replace("-", "_")] = ( # type: ignore[literal-required]
item_node[0].text.replace("\n", " ").strip()
)
# Publication identifier
if len(node.xpath("pub-id")) > 0:
pub_id: list[str] = []
for id_node in node.xpath("pub-id"):
id_type = id_node.get("assigning-authority") or id_node.get(
"pub-id-type"
)
id_text = id_node.text
if id_type and id_text:
pub_id.append(
id_type.replace("\n", " ").strip().upper()
+ ": "
+ id_text.replace("\n", " ").strip()
)
if pub_id:
citation["pub_id"] = ", ".join(pub_id)
# Pages
if len(node.xpath("elocation-id")) > 0:
citation["page"] = (
node.xpath("elocation-id")[0].text.replace("\n", " ").strip()
)
elif len(node.xpath("fpage")) > 0:
citation["page"] = node.xpath("fpage")[0].text.replace("\n", " ").strip()
if len(node.xpath("lpage")) > 0:
citation["page"] += (
"–" + node.xpath("lpage")[0].text.replace("\n", " ").strip()
)
# Flatten the citation to string
text = ""
if citation["author_names"]:
text += citation["author_names"].rstrip(".") + ". "
if citation["title"]:
text += citation["title"] + ". "
if citation["source"]:
text += citation["source"] + ". "
if citation["publisher_name"]:
if citation["publisher_loc"]:
text += f"{citation['publisher_loc']}: "
text += citation["publisher_name"] + ". "
if citation["volume"]:
text = text.rstrip(". ")
text += f" {citation['volume']}. "
if citation["page"]:
text = text.rstrip(". ")
if citation["volume"]:
text += ":"
text += citation["page"] + ". "
if citation["year"]:
text = text.rstrip(". ")
text += f" ({citation['year']})."
if citation["pub_id"]:
text = text.rstrip(".") + ". "
text += citation["pub_id"]
_log.debug("Citation flattened")
return text
def _add_equation(
self, doc: DoclingDocument, parent: NodeItem, node: etree._Element
) -> None:
math_text = node.text
math_parts = math_text.split("$$")
if len(math_parts) == 3:
math_formula = math_parts[1]
doc.add_text(label=DocItemLabel.FORMULA, text=math_formula, parent=parent)
return
def _add_figure_captions(
self, doc: DoclingDocument, parent: NodeItem, node: etree._Element
) -> None:
label_node = node.xpath("label")
label: Optional[str] = (
JatsDocumentBackend._get_text(label_node[0]).strip() if label_node else ""
)
caption_node = node.xpath("caption")
caption: Optional[str]
if len(caption_node) > 0:
caption = ""
for caption_par in list(caption_node[0]):
if caption_par.xpath(".//supplementary-material"):
continue
caption += JatsDocumentBackend._get_text(caption_par).strip() + " "
caption = caption.strip()
else:
caption = None
# TODO: format label vs caption once styling is supported
fig_text: str = f"{label}{' ' if label and caption else ''}{caption}"
fig_caption: Optional[TextItem] = (
doc.add_text(label=DocItemLabel.CAPTION, text=fig_text)
if fig_text
else None
)
doc.add_picture(parent=parent, caption=fig_caption)
return
# TODO: add footnotes when DocItemLabel.FOOTNOTE and styling are supported
# def _add_footnote_group(self, doc: DoclingDocument, parent: NodeItem, node: etree._Element) -> None:
# new_parent = doc.add_group(label=GroupLabel.LIST, name="footnotes", parent=parent)
# for child in node.iterchildren(tag="fn"):
# text = JatsDocumentBackend._get_text(child)
# doc.add_list_item(text=text, parent=new_parent)
def _add_metadata(
self, doc: DoclingDocument, xml_components: XMLComponents
) -> None:
self._add_title(doc, xml_components)
self._add_authors(doc, xml_components)
self._add_abstract(doc, xml_components)
return
def _add_table(
self, doc: DoclingDocument, parent: NodeItem, table_xml_component: Table
) -> None:
soup = BeautifulSoup(table_xml_component["content"], "html.parser")
table_tag = soup.find("table")
if not isinstance(table_tag, Tag):
return
data = HTMLDocumentBackend.parse_table_data(table_tag)
# TODO: format label vs caption once styling is supported
label = table_xml_component["label"]
caption = table_xml_component["caption"]
table_text: str = f"{label}{' ' if label and caption else ''}{caption}"
table_caption: Optional[TextItem] = (
doc.add_text(label=DocItemLabel.CAPTION, text=table_text)
if table_text
else None
)
if data is not None:
doc.add_table(data=data, parent=parent, caption=table_caption)
return
def _add_tables(
self, doc: DoclingDocument, parent: NodeItem, node: etree._Element
) -> None:
table: Table = {"label": "", "caption": "", "content": ""}
# Content
if len(node.xpath("table")) > 0:
table_content_node = node.xpath("table")[0]
elif len(node.xpath("alternatives/table")) > 0:
table_content_node = node.xpath("alternatives/table")[0]
else:
table_content_node = None
if table_content_node is not None:
table["content"] = etree.tostring(table_content_node).decode("utf-8")
# Caption
caption_node = node.xpath("caption")
caption: Optional[str]
if caption_node:
caption = ""
for caption_par in list(caption_node[0]):
if caption_par.xpath(".//supplementary-material"):
continue
caption += JatsDocumentBackend._get_text(caption_par).strip() + " "
caption = caption.strip()
else:
caption = None
if caption is not None:
table["caption"] = caption
# Label
if len(node.xpath("label")) > 0:
table["label"] = node.xpath("label")[0].text
try:
self._add_table(doc, parent, table)
except Exception as e:
_log.warning(f"Skipping unsupported table in {str(self.file)}")
pass
return
def _add_title(self, doc: DoclingDocument, xml_components: XMLComponents) -> None:
self.root = doc.add_text(
parent=None,
text=xml_components["title"],
label=DocItemLabel.TITLE,
)
return
def _walk_linear(
self, doc: DoclingDocument, parent: NodeItem, node: etree._Element
) -> str:
skip_tags = ["term"]
flush_tags = ["ack", "sec", "list", "boxed-text", "disp-formula", "fig"]
new_parent: NodeItem = parent
node_text: str = (
node.text.replace("\n", " ")
if (node.tag not in skip_tags and node.text)
else ""
)
for child in list(node):
stop_walk: bool = False
# flush text into TextItem for some tags in paragraph nodes
if node.tag == "p" and node_text.strip() and child.tag in flush_tags:
doc.add_text(
label=DocItemLabel.TEXT, text=node_text.strip(), parent=parent
)
node_text = ""
# add elements and decide whether to stop walking
if child.tag in ("sec", "ack"):
header = child.xpath("title|label")
text: Optional[str] = None
if len(header) > 0:
text = JatsDocumentBackend._get_text(header[0])
elif child.tag == "ack":
text = DEFAULT_HEADER_ACKNOWLEDGMENTS
if text:
new_parent = doc.add_heading(text=text, parent=parent)
elif child.tag == "list":
new_parent = doc.add_group(
label=GroupLabel.LIST, name="list", parent=parent
)
elif child.tag == "list-item":
# TODO: address any type of content (another list, formula,...)
# TODO: address list type and item label
text = JatsDocumentBackend._get_text(child).strip()
new_parent = doc.add_list_item(text=text, parent=parent)
stop_walk = True
elif child.tag == "fig":
self._add_figure_captions(doc, parent, child)
stop_walk = True
elif child.tag == "table-wrap":
self._add_tables(doc, parent, child)
stop_walk = True
elif child.tag == "suplementary-material":
stop_walk = True
elif child.tag == "fn-group":
# header = child.xpath(".//title") or child.xpath(".//label")
# if header:
# text = JatsDocumentBackend._get_text(header[0])
# fn_parent = doc.add_heading(text=text, parent=new_parent)
# self._add_footnote_group(doc, fn_parent, child)
stop_walk = True
elif child.tag == "ref-list" and node.tag != "ref-list":
header = child.xpath("title|label")
text = (
JatsDocumentBackend._get_text(header[0])
if len(header) > 0
else DEFAULT_HEADER_REFERENCES
)
new_parent = doc.add_heading(text=text, parent=parent)
new_parent = doc.add_group(
parent=new_parent, label=GroupLabel.LIST, name="list"
)
elif child.tag == "element-citation":
text = self._parse_element_citation(child)
self._add_citation(doc, parent, text)
stop_walk = True
elif child.tag == "mixed-citation":
text = JatsDocumentBackend._get_text(child).strip()
self._add_citation(doc, parent, text)
stop_walk = True
elif child.tag == "tex-math":
self._add_equation(doc, parent, child)
stop_walk = True
elif child.tag == "inline-formula":
# TODO: address inline formulas when supported by docling-core
stop_walk = True
# step into child
if not stop_walk:
new_text = self._walk_linear(doc, new_parent, child)
if not (node.getparent().tag == "p" and node.tag in flush_tags):
node_text += new_text
# pick up the tail text
node_text += child.tail.replace("\n", " ") if child.tail else ""
# create paragraph
if node.tag == "p" and node_text.strip():
doc.add_text(label=DocItemLabel.TEXT, text=node_text.strip(), parent=parent)
return ""
else:
# backpropagate the text
return node_text

592
docling/backend/xml/pubmed_backend.py
View File

@ -1,592 +0,0 @@
import logging
from io import BytesIO
from pathlib import Path
from typing import Any, Set, Union
import lxml
from bs4 import BeautifulSoup
from docling_core.types.doc import (
DocItemLabel,
DoclingDocument,
DocumentOrigin,
GroupLabel,
TableCell,
TableData,
)
from lxml import etree
from typing_extensions import TypedDict, override
from docling.backend.abstract_backend import DeclarativeDocumentBackend
from docling.datamodel.base_models import InputFormat
from docling.datamodel.document import InputDocument
_log = logging.getLogger(__name__)
class Paragraph(TypedDict):
text: str
headers: list[str]
class Author(TypedDict):
name: str
affiliation_names: list[str]
class Table(TypedDict):
label: str
caption: str
content: str
class FigureCaption(TypedDict):
label: str
caption: str
class Reference(TypedDict):
author_names: str
title: str
journal: str
year: str
class XMLComponents(TypedDict):
title: str
authors: list[Author]
abstract: str
paragraphs: list[Paragraph]
tables: list[Table]
figure_captions: list[FigureCaption]
references: list[Reference]
class PubMedDocumentBackend(DeclarativeDocumentBackend):
"""
The code from this document backend has been developed by modifying parts of the PubMed Parser library (version 0.5.0, released on 12.08.2024):
Achakulvisut et al., (2020).
Pubmed Parser: A Python Parser for PubMed Open-Access XML Subset and MEDLINE XML Dataset XML Dataset.
Journal of Open Source Software, 5(46), 1979,
https://doi.org/10.21105/joss.01979
"""
@override
def __init__(self, in_doc: "InputDocument", path_or_stream: Union[BytesIO, Path]):
super().__init__(in_doc, path_or_stream)
self.path_or_stream = path_or_stream
# Initialize parents for the document hierarchy
self.parents: dict = {}
self.valid = False
try:
if isinstance(self.path_or_stream, BytesIO):
self.path_or_stream.seek(0)
self.tree: lxml.etree._ElementTree = etree.parse(self.path_or_stream)
if "/NLM//DTD JATS" in self.tree.docinfo.public_id:
self.valid = True
except Exception as exc:
raise RuntimeError(
f"Could not initialize PubMed backend for file with hash {self.document_hash}."
) from exc
@override
def is_valid(self) -> bool:
return self.valid
@classmethod
@override
def supports_pagination(cls) -> bool:
return False
@override
def unload(self):
if isinstance(self.path_or_stream, BytesIO):
self.path_or_stream.close()
self.path_or_stream = None
@classmethod
@override
def supported_formats(cls) -> Set[InputFormat]:
return {InputFormat.XML_PUBMED}
@override
def convert(self) -> DoclingDocument:
# Create empty document
origin = DocumentOrigin(
filename=self.file.name or "file",
mimetype="application/xml",
binary_hash=self.document_hash,
)
doc = DoclingDocument(name=self.file.stem or "file", origin=origin)
_log.debug("Trying to convert PubMed XML document...")
# Get parsed XML components
xml_components: XMLComponents = self._parse()
# Add XML components to the document
doc = self._populate_document(doc, xml_components)
return doc
def _parse_title(self) -> str:
title: str = " ".join(
[
t.replace("\n", "")
for t in self.tree.xpath(".//title-group/article-title")[0].itertext()
]
)
return title
def _parse_authors(self) -> list[Author]:
# Get mapping between affiliation ids and names
affiliation_names = []
for affiliation_node in self.tree.xpath(".//aff[@id]"):
affiliation_names.append(
": ".join([t for t in affiliation_node.itertext() if t != "\n"])
)
affiliation_ids_names = {
id: name
for id, name in zip(self.tree.xpath(".//aff[@id]/@id"), affiliation_names)
}
# Get author names and affiliation names
authors: list[Author] = []
for author_node in self.tree.xpath(
'.//contrib-group/contrib[@contrib-type="author"]'
):
author: Author = {
"name": "",
"affiliation_names": [],
}
# Affiliation names
affiliation_ids = [
a.attrib["rid"] for a in author_node.xpath('xref[@ref-type="aff"]')
]
for id in affiliation_ids:
if id in affiliation_ids_names:
author["affiliation_names"].append(affiliation_ids_names[id])
# Name
author["name"] = (
author_node.xpath("name/surname")[0].text
+ " "
+ author_node.xpath("name/given-names")[0].text
)
authors.append(author)
return authors
def _parse_abstract(self) -> str:
texts = []
for abstract_node in self.tree.xpath(".//abstract"):
for text in abstract_node.itertext():
texts.append(text.replace("\n", ""))
abstract: str = "".join(texts)
return abstract
def _parse_main_text(self) -> list[Paragraph]:
paragraphs: list[Paragraph] = []
for paragraph_node in self.tree.xpath("//body//p"):
# Skip captions
if "/caption" in paragraph_node.getroottree().getpath(paragraph_node):
continue
paragraph: Paragraph = {"text": "", "headers": []}
# Text
paragraph["text"] = "".join(
[t.replace("\n", "") for t in paragraph_node.itertext()]
)
# Header
path = "../title"
while len(paragraph_node.xpath(path)) > 0:
paragraph["headers"].append(
"".join(
[
t.replace("\n", "")
for t in paragraph_node.xpath(path)[0].itertext()
]
)
)
path = "../" + path
paragraphs.append(paragraph)
return paragraphs
def _parse_tables(self) -> list[Table]:
tables: list[Table] = []
for table_node in self.tree.xpath(".//body//table-wrap"):
table: Table = {"label": "", "caption": "", "content": ""}
# Content
if len(table_node.xpath("table")) > 0:
table_content_node = table_node.xpath("table")[0]
elif len(table_node.xpath("alternatives/table")) > 0:
table_content_node = table_node.xpath("alternatives/table")[0]
else:
table_content_node = None
if table_content_node != None:
table["content"] = etree.tostring(table_content_node).decode("utf-8")
# Caption
if len(table_node.xpath("caption/p")) > 0:
caption_node = table_node.xpath("caption/p")[0]
elif len(table_node.xpath("caption/title")) > 0:
caption_node = table_node.xpath("caption/title")[0]
else:
caption_node = None
if caption_node != None:
table["caption"] = "".join(
[t.replace("\n", "") for t in caption_node.itertext()]
)
# Label
if len(table_node.xpath("label")) > 0:
table["label"] = table_node.xpath("label")[0].text
tables.append(table)
return tables
def _parse_figure_captions(self) -> list[FigureCaption]:
figure_captions: list[FigureCaption] = []
if not (self.tree.xpath(".//fig")):
return figure_captions
for figure_node in self.tree.xpath(".//fig"):
figure_caption: FigureCaption = {
"caption": "",
"label": "",
}
# Label
if figure_node.xpath("label"):
figure_caption["label"] = "".join(
[
t.replace("\n", "")
for t in figure_node.xpath("label")[0].itertext()
]
)
# Caption
if figure_node.xpath("caption"):
caption = ""
for caption_node in figure_node.xpath("caption")[0].getchildren():
caption += (
"".join([t.replace("\n", "") for t in caption_node.itertext()])
+ "\n"
)
figure_caption["caption"] = caption
figure_captions.append(figure_caption)
return figure_captions
def _parse_references(self) -> list[Reference]:
references: list[Reference] = []
for reference_node_abs in self.tree.xpath(".//ref-list/ref"):
reference: Reference = {
"author_names": "",
"title": "",
"journal": "",
"year": "",
}
reference_node: Any = None
for tag in ["mixed-citation", "element-citation", "citation"]:
if len(reference_node_abs.xpath(tag)) > 0:
reference_node = reference_node_abs.xpath(tag)[0]
break
if reference_node is None:
continue
if all(
not (ref_type in ["citation-type", "publication-type"])
for ref_type in reference_node.attrib.keys()
):
continue
# Author names
names = []
if len(reference_node.xpath("name")) > 0:
for name_node in reference_node.xpath("name"):
name_str = " ".join(
[t.text for t in name_node.getchildren() if (t.text != None)]
)
names.append(name_str)
elif len(reference_node.xpath("person-group")) > 0:
for name_node in reference_node.xpath("person-group")[0]:
name_str = (
name_node.xpath("given-names")[0].text
+ " "
+ name_node.xpath("surname")[0].text
)
names.append(name_str)
reference["author_names"] = "; ".join(names)
# Title
if len(reference_node.xpath("article-title")) > 0:
reference["title"] = " ".join(
[
t.replace("\n", " ")
for t in reference_node.xpath("article-title")[0].itertext()
]
)
# Journal
if len(reference_node.xpath("source")) > 0:
reference["journal"] = reference_node.xpath("source")[0].text
# Year
if len(reference_node.xpath("year")) > 0:
reference["year"] = reference_node.xpath("year")[0].text
if (
not (reference_node.xpath("article-title"))
and not (reference_node.xpath("journal"))
and not (reference_node.xpath("year"))
):
reference["title"] = reference_node.text
references.append(reference)
return references
def _parse(self) -> XMLComponents:
"""Parsing PubMed document."""
xml_components: XMLComponents = {
"title": self._parse_title(),
"authors": self._parse_authors(),
"abstract": self._parse_abstract(),
"paragraphs": self._parse_main_text(),
"tables": self._parse_tables(),
"figure_captions": self._parse_figure_captions(),
"references": self._parse_references(),
}
return xml_components
def _populate_document(
self, doc: DoclingDocument, xml_components: XMLComponents
) -> DoclingDocument:
self._add_title(doc, xml_components)
self._add_authors(doc, xml_components)
self._add_abstract(doc, xml_components)
self._add_main_text(doc, xml_components)
if xml_components["tables"]:
self._add_tables(doc, xml_components)
if xml_components["figure_captions"]:
self._add_figure_captions(doc, xml_components)
self._add_references(doc, xml_components)
return doc
def _add_figure_captions(
self, doc: DoclingDocument, xml_components: XMLComponents
) -> None:
self.parents["Figures"] = doc.add_heading(
parent=self.parents["Title"], text="Figures"
)
for figure_caption_xml_component in xml_components["figure_captions"]:
figure_caption_text = (
figure_caption_xml_component["label"]
+ ": "
+ figure_caption_xml_component["caption"].strip()
)
fig_caption = doc.add_text(
label=DocItemLabel.CAPTION, text=figure_caption_text
)
doc.add_picture(
parent=self.parents["Figures"],
caption=fig_caption,
)
return
def _add_title(self, doc: DoclingDocument, xml_components: XMLComponents) -> None:
self.parents["Title"] = doc.add_text(
parent=None,
text=xml_components["title"],
label=DocItemLabel.TITLE,
)
return
def _add_authors(self, doc: DoclingDocument, xml_components: XMLComponents) -> None:
authors_affiliations: list = []
for author in xml_components["authors"]:
authors_affiliations.append(author["name"])
authors_affiliations.append(", ".join(author["affiliation_names"]))
authors_affiliations_str = "; ".join(authors_affiliations)
doc.add_text(
parent=self.parents["Title"],
text=authors_affiliations_str,
label=DocItemLabel.PARAGRAPH,
)
return
def _add_abstract(
self, doc: DoclingDocument, xml_components: XMLComponents
) -> None:
abstract_text: str = xml_components["abstract"]
self.parents["Abstract"] = doc.add_heading(
parent=self.parents["Title"], text="Abstract"
)
doc.add_text(
parent=self.parents["Abstract"],
text=abstract_text,
label=DocItemLabel.TEXT,
)
return
def _add_main_text(
self, doc: DoclingDocument, xml_components: XMLComponents
) -> None:
added_headers: list = []
for paragraph in xml_components["paragraphs"]:
if not (paragraph["headers"]):
continue
# Header
for i, header in enumerate(reversed(paragraph["headers"])):
if header in added_headers:
continue
added_headers.append(header)
if ((i - 1) >= 0) and list(reversed(paragraph["headers"]))[
i - 1
] in self.parents:
parent = self.parents[list(reversed(paragraph["headers"]))[i - 1]]
else:
parent = self.parents["Title"]
self.parents[header] = doc.add_heading(parent=parent, text=header)
# Paragraph text
if paragraph["headers"][0] in self.parents:
parent = self.parents[paragraph["headers"][0]]
else:
parent = self.parents["Title"]
doc.add_text(parent=parent, label=DocItemLabel.TEXT, text=paragraph["text"])
return
def _add_references(
self, doc: DoclingDocument, xml_components: XMLComponents
) -> None:
self.parents["References"] = doc.add_heading(
parent=self.parents["Title"], text="References"
)
current_list = doc.add_group(
parent=self.parents["References"], label=GroupLabel.LIST, name="list"
)
for reference in xml_components["references"]:
reference_text: str = ""
if reference["author_names"]:
reference_text += reference["author_names"] + ". "
if reference["title"]:
reference_text += reference["title"]
if reference["title"][-1] != ".":
reference_text += "."
reference_text += " "
if reference["journal"]:
reference_text += reference["journal"]
if reference["year"]:
reference_text += " (" + reference["year"] + ")"
if not (reference_text):
_log.debug(f"Skipping reference for: {str(self.file)}")
continue
doc.add_list_item(
text=reference_text, enumerated=False, parent=current_list
)
return
def _add_tables(self, doc: DoclingDocument, xml_components: XMLComponents) -> None:
self.parents["Tables"] = doc.add_heading(
parent=self.parents["Title"], text="Tables"
)
for table_xml_component in xml_components["tables"]:
try:
self._add_table(doc, table_xml_component)
except Exception as e:
_log.debug(f"Skipping unsupported table for: {str(self.file)}")
pass
return
def _add_table(self, doc: DoclingDocument, table_xml_component: Table) -> None:
soup = BeautifulSoup(table_xml_component["content"], "html.parser")
table_tag = soup.find("table")
nested_tables = table_tag.find("table")
if nested_tables:
_log.debug(f"Skipping nested table for: {str(self.file)}")
return
# Count the number of rows (number of <tr> elements)
num_rows = len(table_tag.find_all("tr"))
# Find the number of columns (taking into account colspan)
num_cols = 0
for row in table_tag.find_all("tr"):
col_count = 0
for cell in row.find_all(["td", "th"]):
colspan = int(cell.get("colspan", 1))
col_count += colspan
num_cols = max(num_cols, col_count)
grid = [[None for _ in range(num_cols)] for _ in range(num_rows)]
data = TableData(num_rows=num_rows, num_cols=num_cols, table_cells=[])
# Iterate over the rows in the table
for row_idx, row in enumerate(table_tag.find_all("tr")):
# For each row, find all the column cells (both <td> and <th>)
cells = row.find_all(["td", "th"])
# Check if each cell in the row is a header -> means it is a column header
col_header = True
for j, html_cell in enumerate(cells):
if html_cell.name == "td":
col_header = False
# Extract and print the text content of each cell
col_idx = 0
for _, html_cell in enumerate(cells):
text = html_cell.text
col_span = int(html_cell.get("colspan", 1))
row_span = int(html_cell.get("rowspan", 1))
while grid[row_idx][col_idx] != None:
col_idx += 1
for r in range(row_span):
for c in range(col_span):
grid[row_idx + r][col_idx + c] = text
cell = TableCell(
text=text,
row_span=row_span,
col_span=col_span,
start_row_offset_idx=row_idx,
end_row_offset_idx=row_idx + row_span,
start_col_offset_idx=col_idx,
end_col_offset_idx=col_idx + col_span,
col_header=col_header,
row_header=((not col_header) and html_cell.name == "th"),
)
data.table_cells.append(cell)
table_caption = doc.add_text(
label=DocItemLabel.CAPTION,
text=table_xml_component["label"] + ": " + table_xml_component["caption"],
)
doc.add_table(data=data, parent=self.parents["Tables"], caption=table_caption)
return

75
docling/backend/xml/uspto_backend.py
View File

@ -14,7 +14,7 @@ from abc import ABC, abstractmethod
from enum import Enum, unique
from io import BytesIO
from pathlib import Path
from typing import Any, Final, Optional, Union
from typing import Final, Optional, Union
from bs4 import BeautifulSoup, Tag
from docling_core.types.doc import (
@ -1406,6 +1406,10 @@ class XmlTable:
http://oasis-open.org/specs/soextblx.dtd
"""
class ColInfo(TypedDict):
ncols: int
colinfo: list[dict]
class MinColInfoType(TypedDict):
offset: list[int]
colwidth: list[int]
@ -1425,7 +1429,7 @@ class XmlTable:
self.empty_text = ""
self._soup = BeautifulSoup(input, features="xml")
def _create_tg_range(self, tgs: list[dict[str, Any]]) -> dict[int, ColInfoType]:
def _create_tg_range(self, tgs: list[ColInfo]) -> dict[int, ColInfoType]:
"""Create a unified range along the table groups.
Args:
@ -1532,19 +1536,26 @@ class XmlTable:
Returns:
A docling table object.
"""
tgs_align = []
tg_secs = table.find_all("tgroup")
tgs_align: list[XmlTable.ColInfo] = []
tg_secs = table("tgroup")
if tg_secs:
for tg_sec in tg_secs:
ncols = tg_sec.get("cols", None)
if ncols:
ncols = int(ncols)
tg_align = {"ncols": ncols, "colinfo": []}
cs_secs = tg_sec.find_all("colspec")
if not isinstance(tg_sec, Tag):
continue
col_val = tg_sec.get("cols")
ncols = (
int(col_val)
if isinstance(col_val, str) and col_val.isnumeric()
else 1
)
tg_align: XmlTable.ColInfo = {"ncols": ncols, "colinfo": []}
cs_secs = tg_sec("colspec")
if cs_secs:
for cs_sec in cs_secs:
colname = cs_sec.get("colname", None)
colwidth = cs_sec.get("colwidth", None)
if not isinstance(cs_sec, Tag):
continue
colname = cs_sec.get("colname")
colwidth = cs_sec.get("colwidth")
tg_align["colinfo"].append(
{"colname": colname, "colwidth": colwidth}
)
@ -1565,16 +1576,23 @@ class XmlTable:
table_data: list[TableCell] = []
i_row_global = 0
is_row_empty: bool = True
tg_secs = table.find_all("tgroup")
tg_secs = table("tgroup")
if tg_secs:
for itg, tg_sec in enumerate(tg_secs):
if not isinstance(tg_sec, Tag):
continue
tg_range = tgs_range[itg]
row_secs = tg_sec.find_all(["row", "tr"])
row_secs = tg_sec(["row", "tr"])
if row_secs:
for row_sec in row_secs:
entry_secs = row_sec.find_all(["entry", "td"])
is_header: bool = row_sec.parent.name in ["thead"]
if not isinstance(row_sec, Tag):
continue
entry_secs = row_sec(["entry", "td"])
is_header: bool = (
row_sec.parent is not None
and row_sec.parent.name == "thead"
)
ncols = 0
local_row: list[TableCell] = []
@ -1582,23 +1600,26 @@ class XmlTable:
if entry_secs:
wrong_nbr_cols = False
for ientry, entry_sec in enumerate(entry_secs):
if not isinstance(entry_sec, Tag):
continue
text = entry_sec.get_text().strip()
# start-end
namest = entry_sec.attrs.get("namest", None)
nameend = entry_sec.attrs.get("nameend", None)
if isinstance(namest, str) and namest.isnumeric():
namest = int(namest)
else:
namest = ientry + 1
namest = entry_sec.get("namest")
nameend = entry_sec.get("nameend")
start = (
int(namest)
if isinstance(namest, str) and namest.isnumeric()
else ientry + 1
)
if isinstance(nameend, str) and nameend.isnumeric():
nameend = int(nameend)
end = int(nameend)
shift = 0
else:
nameend = ientry + 2
end = ientry + 2
shift = 1
if nameend > len(tg_range["cell_offst"]):
if end > len(tg_range["cell_offst"]):
wrong_nbr_cols = True
self.nbr_messages += 1
if self.nbr_messages <= self.max_nbr_messages:
@ -1608,8 +1629,8 @@ class XmlTable:
break
range_ = [
tg_range["cell_offst"][namest - 1],
tg_range["cell_offst"][nameend - 1] - shift,
tg_range["cell_offst"][start - 1],
tg_range["cell_offst"][end - 1] - shift,
]
# add row and replicate cell if needed
@ -1668,7 +1689,7 @@ class XmlTable:
A docling table data.
"""
section = self._soup.find("table")
if section is not None:
if isinstance(section, Tag):
table = self._parse_table(section)
if table.num_rows == 0 or table.num_cols == 0:
_log.warning("The parsed USPTO table is empty")

20
docling/cli/main.py
View File

@ -219,10 +219,27 @@ def convert(
bool,
typer.Option(..., help="Enable the formula enrichment model in the pipeline."),
] = False,
enrich_picture_classes: Annotated[
bool,
typer.Option(
...,
help="Enable the picture classification enrichment model in the pipeline.",
),
] = False,
enrich_picture_description: Annotated[
bool,
typer.Option(..., help="Enable the picture description model in the pipeline."),
] = False,
artifacts_path: Annotated[
Optional[Path],
typer.Option(..., help="If provided, the location of the model artifacts."),
] = None,
enable_remote_services: Annotated[
bool,
typer.Option(
..., help="Must be enabled when using models connecting to remote services."
),
] = False,
abort_on_error: Annotated[
bool,
typer.Option(
@ -369,12 +386,15 @@ def convert(
accelerator_options = AcceleratorOptions(num_threads=num_threads, device=device)
pipeline_options = PdfPipelineOptions(
enable_remote_services=enable_remote_services,
accelerator_options=accelerator_options,
do_ocr=ocr,
ocr_options=ocr_options,
do_table_structure=True,
do_code_enrichment=enrich_code,
do_formula_enrichment=enrich_formula,
do_picture_description=enrich_picture_description,
do_picture_classification=enrich_picture_classes,
document_timeout=document_timeout,
)
pipeline_options.table_structure_options.do_cell_matching = (

131
docling/cli/models.py Normal file
View File

@ -0,0 +1,131 @@
import logging
import warnings
from enum import Enum
from pathlib import Path
from typing import Annotated, Optional
import typer
from rich.console import Console
from rich.logging import RichHandler
from docling.datamodel.settings import settings
from docling.utils.model_downloader import download_models
warnings.filterwarnings(action="ignore", category=UserWarning, module="pydantic|torch")
warnings.filterwarnings(action="ignore", category=FutureWarning, module="easyocr")
console = Console()
err_console = Console(stderr=True)
app = typer.Typer(
name="Docling models helper",
no_args_is_help=True,
add_completion=False,
pretty_exceptions_enable=False,
)
class _AvailableModels(str, Enum):
LAYOUT = "layout"
TABLEFORMER = "tableformer"
CODE_FORMULA = "code_formula"
PICTURE_CLASSIFIER = "picture_classifier"
SMOLVLM = "smolvlm"
GRANITE_VISION = "granite_vision"
EASYOCR = "easyocr"
_default_models = [
_AvailableModels.LAYOUT,
_AvailableModels.TABLEFORMER,
_AvailableModels.CODE_FORMULA,
_AvailableModels.PICTURE_CLASSIFIER,
_AvailableModels.EASYOCR,
]
@app.command("download")
def download(
output_dir: Annotated[
Path,
typer.Option(
...,
"-o",
"--output-dir",
help="The directory where to download the models.",
),
] = (settings.cache_dir / "models"),
force: Annotated[
bool, typer.Option(..., help="If true, the download will be forced.")
] = False,
models: Annotated[
Optional[list[_AvailableModels]],
typer.Argument(
help=f"Models to download (default behavior: a predefined set of models will be downloaded).",
),
] = None,
all: Annotated[
bool,
typer.Option(
...,
"--all",
help="If true, all available models will be downloaded (mutually exclusive with passing specific models).",
show_default=True,
),
] = False,
quiet: Annotated[
bool,
typer.Option(
...,
"-q",
"--quiet",
help="No extra output is generated, the CLI prints only the directory with the cached models.",
),
] = False,
):
if models and all:
raise typer.BadParameter(
"Cannot simultaneously set 'all' parameter and specify models to download."
)
if not quiet:
FORMAT = "%(message)s"
logging.basicConfig(
level=logging.INFO,
format="[blue]%(message)s[/blue]",
datefmt="[%X]",
handlers=[RichHandler(show_level=False, show_time=False, markup=True)],
)
to_download = models or ([m for m in _AvailableModels] if all else _default_models)
output_dir = download_models(
output_dir=output_dir,
force=force,
progress=(not quiet),
with_layout=_AvailableModels.LAYOUT in to_download,
with_tableformer=_AvailableModels.TABLEFORMER in to_download,
with_code_formula=_AvailableModels.CODE_FORMULA in to_download,
with_picture_classifier=_AvailableModels.PICTURE_CLASSIFIER in to_download,
with_smolvlm=_AvailableModels.SMOLVLM in to_download,
with_granite_vision=_AvailableModels.GRANITE_VISION in to_download,
with_easyocr=_AvailableModels.EASYOCR in to_download,
)
if quiet:
typer.echo(output_dir)
else:
typer.secho(f"\nModels downloaded into: {output_dir}.", fg="green")
console.print(
"\n",
"Docling can now be configured for running offline using the local artifacts.\n\n",
"Using the CLI:",
f"`docling --artifacts-path={output_dir} FILE`",
"\n",
"Using Python: see the documentation at <https://ds4sd.github.io/docling/usage>.",
)
click_app = typer.main.get_command(app)
if __name__ == "__main__":
app()

17
docling/cli/tools.py Normal file
View File

@ -0,0 +1,17 @@
import typer
from docling.cli.models import app as models_app
app = typer.Typer(
name="Docling helpers",
no_args_is_help=True,
add_completion=False,
pretty_exceptions_enable=False,
)
app.add_typer(models_app, name="models")
click_app = typer.main.get_command(app)
if __name__ == "__main__":
app()

14
docling/datamodel/base_models.py
View File

@ -34,13 +34,14 @@ class InputFormat(str, Enum):
DOCX = "docx"
PPTX = "pptx"
HTML = "html"
XML_PUBMED = "xml_pubmed"
IMAGE = "image"
PDF = "pdf"
ASCIIDOC = "asciidoc"
MD = "md"
CSV = "csv"
XLSX = "xlsx"
XML_USPTO = "xml_uspto"
XML_JATS = "xml_jats"
JSON_DOCLING = "json_docling"
@ -58,9 +59,10 @@ FormatToExtensions: Dict[InputFormat, List[str]] = {
InputFormat.PDF: ["pdf"],
InputFormat.MD: ["md"],
InputFormat.HTML: ["html", "htm", "xhtml"],
InputFormat.XML_PUBMED: ["xml", "nxml"],
InputFormat.XML_JATS: ["xml", "nxml"],
InputFormat.IMAGE: ["jpg", "jpeg", "png", "tif", "tiff", "bmp"],
InputFormat.ASCIIDOC: ["adoc", "asciidoc", "asc"],
InputFormat.CSV: ["csv"],
InputFormat.XLSX: ["xlsx"],
InputFormat.XML_USPTO: ["xml", "txt"],
InputFormat.JSON_DOCLING: ["json"],
@ -77,7 +79,7 @@ FormatToMimeType: Dict[InputFormat, List[str]] = {
"application/vnd.openxmlformats-officedocument.presentationml.presentation",
],
InputFormat.HTML: ["text/html", "application/xhtml+xml"],
InputFormat.XML_PUBMED: ["application/xml"],
InputFormat.XML_JATS: ["application/xml"],
InputFormat.IMAGE: [
"image/png",
"image/jpeg",
@ -88,6 +90,7 @@ FormatToMimeType: Dict[InputFormat, List[str]] = {
InputFormat.PDF: ["application/pdf"],
InputFormat.ASCIIDOC: ["text/asciidoc"],
InputFormat.MD: ["text/markdown", "text/x-markdown"],
InputFormat.CSV: ["text/csv"],
InputFormat.XLSX: [
"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
],
@ -151,6 +154,10 @@ class LayoutPrediction(BaseModel):
clusters: List[Cluster] = []
class VlmPrediction(BaseModel):
text: str = ""
class ContainerElement(
BasePageElement
): # Used for Form and Key-Value-Regions, only for typing.
@ -194,6 +201,7 @@ class PagePredictions(BaseModel):
tablestructure: Optional[TableStructurePrediction] = None
figures_classification: Optional[FigureClassificationPrediction] = None
equations_prediction: Optional[EquationPrediction] = None
vlm_response: Optional[VlmPrediction] = None
PageElement = Union[TextElement, Table, FigureElement, ContainerElement]

43
docling/datamodel/document.py
View File

@ -1,3 +1,4 @@
import csv
import logging
import re
from enum import Enum
@ -157,6 +158,8 @@ class InputDocument(BaseModel):
self.page_count = self._backend.page_count()
if not self.page_count <= self.limits.max_num_pages:
self.valid = False
elif self.page_count < self.limits.page_range[0]:
self.valid = False
except (FileNotFoundError, OSError) as e:
self.valid = False
@ -294,6 +297,7 @@ class _DocumentConversionInput(BaseModel):
mime = _DocumentConversionInput._mime_from_extension(ext)
mime = mime or _DocumentConversionInput._detect_html_xhtml(content)
mime = mime or _DocumentConversionInput._detect_csv(content)
mime = mime or "text/plain"
formats = MimeTypeToFormat.get(mime, [])
if formats:
@ -329,11 +333,11 @@ class _DocumentConversionInput(BaseModel):
):
input_format = InputFormat.XML_USPTO
if (
InputFormat.XML_PUBMED in formats
and "/NLM//DTD JATS" in xml_doctype
if InputFormat.XML_JATS in formats and (
"JATS-journalpublishing" in xml_doctype
or "JATS-archive" in xml_doctype
):
input_format = InputFormat.XML_PUBMED
input_format = InputFormat.XML_JATS
elif mime == "text/plain":
if InputFormat.XML_USPTO in formats and content_str.startswith("PATN\r\n"):
@ -350,6 +354,8 @@ class _DocumentConversionInput(BaseModel):
mime = FormatToMimeType[InputFormat.HTML][0]
elif ext in FormatToExtensions[InputFormat.MD]:
mime = FormatToMimeType[InputFormat.MD][0]
elif ext in FormatToExtensions[InputFormat.CSV]:
mime = FormatToMimeType[InputFormat.CSV][0]
elif ext in FormatToExtensions[InputFormat.JSON_DOCLING]:
mime = FormatToMimeType[InputFormat.JSON_DOCLING][0]
elif ext in FormatToExtensions[InputFormat.PDF]:
@ -390,3 +396,32 @@ class _DocumentConversionInput(BaseModel):
return "application/xml"
return None
@staticmethod
def _detect_csv(
content: bytes,
) -> Optional[Literal["text/csv"]]:
"""Guess the mime type of a CSV file from its content.
Args:
content: A short piece of a document from its beginning.
Returns:
The mime type of a CSV file, or None if the content does
not match any of the format.
"""
content_str = content.decode("ascii", errors="ignore").strip()
# Ensure there's at least one newline (CSV is usually multi-line)
if "\n" not in content_str:
return None
# Use csv.Sniffer to detect CSV characteristics
try:
dialect = csv.Sniffer().sniff(content_str)
if dialect.delimiter in {",", ";", "\t", "|"}: # Common delimiters
return "text/csv"
except csv.Error:
return None
return None

149
docling/datamodel/pipeline_options.py
View File

@ -1,11 +1,26 @@
import logging
import os
import re
import warnings
from enum import Enum
from pathlib import Path
from typing import Any, List, Literal, Optional, Union
from typing import Annotated, Any, Dict, List, Literal, Optional, Union
from pydantic import BaseModel, ConfigDict, Field, model_validator
from pydantic_settings import BaseSettings, SettingsConfigDict
from pydantic import (
AnyUrl,
BaseModel,
ConfigDict,
Field,
field_validator,
model_validator,
validator,
)
from pydantic_settings import (
BaseSettings,
PydanticBaseSettingsSource,
SettingsConfigDict,
)
from typing_extensions import deprecated
_log = logging.getLogger(__name__)
@ -25,7 +40,19 @@ class AcceleratorOptions(BaseSettings):
)
num_threads: int = 4
device: AcceleratorDevice = AcceleratorDevice.AUTO
device: Union[str, AcceleratorDevice] = "auto"
cuda_use_flash_attention2: bool = False
@field_validator("device")
def validate_device(cls, value):
# "auto", "cpu", "cuda", "mps", or "cuda:N"
if value in {d.value for d in AcceleratorDevice} or re.match(
r"^cuda(:\d+)?$", value
):
return value
raise ValueError(
"Invalid device option. Use 'auto', 'cpu', 'mps', 'cuda', or 'cuda:N'."
)
@model_validator(mode="before")
@classmethod
@ -41,7 +68,6 @@ class AcceleratorOptions(BaseSettings):
"""
if isinstance(data, dict):
input_num_threads = data.get("num_threads")
# Check if to set the num_threads from the alternative envvar
if input_num_threads is None:
docling_num_threads = os.getenv("DOCLING_NUM_THREADS")
@ -184,6 +210,90 @@ class OcrMacOptions(OcrOptions):
)
class PictureDescriptionBaseOptions(BaseModel):
kind: str
batch_size: int = 8
scale: float = 2
bitmap_area_threshold: float = (
0.2 # percentage of the area for a bitmap to processed with the models
)
class PictureDescriptionApiOptions(PictureDescriptionBaseOptions):
kind: Literal["api"] = "api"
url: AnyUrl = AnyUrl("http://localhost:8000/v1/chat/completions")
headers: Dict[str, str] = {}
params: Dict[str, Any] = {}
timeout: float = 20
prompt: str = "Describe this image in a few sentences."
provenance: str = ""
class PictureDescriptionVlmOptions(PictureDescriptionBaseOptions):
kind: Literal["vlm"] = "vlm"
repo_id: str
prompt: str = "Describe this image in a few sentences."
# Config from here https://huggingface.co/docs/transformers/en/main_classes/text_generation#transformers.GenerationConfig
generation_config: Dict[str, Any] = dict(max_new_tokens=200, do_sample=False)
@property
def repo_cache_folder(self) -> str:
return self.repo_id.replace("/", "--")
smolvlm_picture_description = PictureDescriptionVlmOptions(
repo_id="HuggingFaceTB/SmolVLM-256M-Instruct"
)
# phi_picture_description = PictureDescriptionVlmOptions(repo_id="microsoft/Phi-3-vision-128k-instruct")
granite_picture_description = PictureDescriptionVlmOptions(
repo_id="ibm-granite/granite-vision-3.1-2b-preview",
prompt="What is shown in this image?",
)
class BaseVlmOptions(BaseModel):
kind: str
prompt: str
class ResponseFormat(str, Enum):
DOCTAGS = "doctags"
MARKDOWN = "markdown"
class HuggingFaceVlmOptions(BaseVlmOptions):
kind: Literal["hf_model_options"] = "hf_model_options"
repo_id: str
load_in_8bit: bool = True
llm_int8_threshold: float = 6.0
quantized: bool = False
response_format: ResponseFormat
@property
def repo_cache_folder(self) -> str:
return self.repo_id.replace("/", "--")
smoldocling_vlm_conversion_options = HuggingFaceVlmOptions(
repo_id="ds4sd/SmolDocling-256M-preview",
prompt="Convert this page to docling.",
response_format=ResponseFormat.DOCTAGS,
)
granite_vision_vlm_conversion_options = HuggingFaceVlmOptions(
repo_id="ibm-granite/granite-vision-3.1-2b-preview",
# prompt="OCR the full page to markdown.",
prompt="OCR this image.",
response_format=ResponseFormat.MARKDOWN,
)
# Define an enum for the backend options
class PdfBackend(str, Enum):
"""Enum of valid PDF backends."""
@ -212,9 +322,27 @@ class PipelineOptions(BaseModel):
)
document_timeout: Optional[float] = None
accelerator_options: AcceleratorOptions = AcceleratorOptions()
enable_remote_services: bool = False
class PdfPipelineOptions(PipelineOptions):
class PaginatedPipelineOptions(PipelineOptions):
images_scale: float = 1.0
generate_page_images: bool = False
generate_picture_images: bool = False
class VlmPipelineOptions(PaginatedPipelineOptions):
artifacts_path: Optional[Union[Path, str]] = None
generate_page_images: bool = True
force_backend_text: bool = (
False # (To be used with vlms, or other generative models)
)
# If True, text from backend will be used instead of generated text
vlm_options: Union[HuggingFaceVlmOptions] = smoldocling_vlm_conversion_options
class PdfPipelineOptions(PaginatedPipelineOptions):
"""Options for the PDF pipeline."""
artifacts_path: Optional[Union[Path, str]] = None
@ -223,6 +351,11 @@ class PdfPipelineOptions(PipelineOptions):
do_code_enrichment: bool = False # True: perform code OCR
do_formula_enrichment: bool = False # True: perform formula OCR, return Latex code
do_picture_classification: bool = False # True: classify pictures in documents
do_picture_description: bool = False # True: run describe pictures in documents
force_backend_text: bool = (
False # (To be used with vlms, or other generative models)
)
# If True, text from backend will be used instead of generated text
table_structure_options: TableStructureOptions = TableStructureOptions()
ocr_options: Union[
@ -232,6 +365,10 @@ class PdfPipelineOptions(PipelineOptions):
OcrMacOptions,
RapidOcrOptions,
] = Field(EasyOcrOptions(), discriminator="kind")
picture_description_options: Annotated[
Union[PictureDescriptionApiOptions, PictureDescriptionVlmOptions],
Field(discriminator="kind"),
] = smolvlm_picture_description
images_scale: float = 1.0
generate_page_images: bool = False

20
docling/datamodel/settings.py
View File

@ -1,13 +1,28 @@
import sys
from pathlib import Path
from typing import Annotated, Optional, Tuple
from pydantic import BaseModel
from pydantic import BaseModel, PlainValidator
from pydantic_settings import BaseSettings, SettingsConfigDict
def _validate_page_range(v: Tuple[int, int]) -> Tuple[int, int]:
if v[0] < 1 or v[1] < v[0]:
raise ValueError(
"Invalid page range: start must be ≥ 1 and end must be ≥ start."
)
return v
PageRange = Annotated[Tuple[int, int], PlainValidator(_validate_page_range)]
DEFAULT_PAGE_RANGE: PageRange = (1, sys.maxsize)
class DocumentLimits(BaseModel):
max_num_pages: int = sys.maxsize
max_file_size: int = sys.maxsize
page_range: PageRange = DEFAULT_PAGE_RANGE
class BatchConcurrencySettings(BaseModel):
@ -46,5 +61,8 @@ class AppSettings(BaseSettings):
perf: BatchConcurrencySettings
debug: DebugSettings
cache_dir: Path = Path.home() / ".cache" / "docling"
artifacts_path: Optional[Path] = None
settings = AppSettings(perf=BatchConcurrencySettings(), debug=DebugSettings())

33
docling/document_converter.py
View File

@ -1,14 +1,16 @@
import logging
import math
import sys
import time
from functools import partial
from pathlib import Path
from typing import Dict, Iterable, Iterator, List, Optional, Type, Union
from typing import Dict, Iterable, Iterator, List, Optional, Tuple, Type, Union
from pydantic import BaseModel, ConfigDict, model_validator, validate_call
from docling.backend.abstract_backend import AbstractDocumentBackend
from docling.backend.asciidoc_backend import AsciiDocBackend
from docling.backend.csv_backend import CsvDocumentBackend
from docling.backend.docling_parse_v2_backend import DoclingParseV2DocumentBackend
from docling.backend.html_backend import HTMLDocumentBackend
from docling.backend.json.docling_json_backend import DoclingJSONBackend
@ -16,7 +18,7 @@ from docling.backend.md_backend import MarkdownDocumentBackend
from docling.backend.msexcel_backend import MsExcelDocumentBackend
from docling.backend.mspowerpoint_backend import MsPowerpointDocumentBackend
from docling.backend.msword_backend import MsWordDocumentBackend
from docling.backend.xml.pubmed_backend import PubMedDocumentBackend
from docling.backend.xml.jats_backend import JatsDocumentBackend
from docling.backend.xml.uspto_backend import PatentUsptoDocumentBackend
from docling.datamodel.base_models import (
ConversionStatus,
@ -31,7 +33,12 @@ from docling.datamodel.document import (
_DocumentConversionInput,
)
from docling.datamodel.pipeline_options import PipelineOptions
from docling.datamodel.settings import DocumentLimits, settings
from docling.datamodel.settings import (
DEFAULT_PAGE_RANGE,
DocumentLimits,
PageRange,
settings,
)
from docling.exceptions import ConversionError
from docling.pipeline.base_pipeline import BasePipeline
from docling.pipeline.simple_pipeline import SimplePipeline
@ -55,6 +62,11 @@ class FormatOption(BaseModel):
return self
class CsvFormatOption(FormatOption):
pipeline_cls: Type = SimplePipeline
backend: Type[AbstractDocumentBackend] = CsvDocumentBackend
class ExcelFormatOption(FormatOption):
pipeline_cls: Type = SimplePipeline
backend: Type[AbstractDocumentBackend] = MsExcelDocumentBackend
@ -90,9 +102,9 @@ class PatentUsptoFormatOption(FormatOption):
backend: Type[PatentUsptoDocumentBackend] = PatentUsptoDocumentBackend
class XMLPubMedFormatOption(FormatOption):
class XMLJatsFormatOption(FormatOption):
pipeline_cls: Type = SimplePipeline
backend: Type[AbstractDocumentBackend] = PubMedDocumentBackend
backend: Type[AbstractDocumentBackend] = JatsDocumentBackend
class ImageFormatOption(FormatOption):
@ -107,6 +119,9 @@ class PdfFormatOption(FormatOption):
def _get_default_option(format: InputFormat) -> FormatOption:
format_to_default_options = {
InputFormat.CSV: FormatOption(
pipeline_cls=SimplePipeline, backend=CsvDocumentBackend
),
InputFormat.XLSX: FormatOption(
pipeline_cls=SimplePipeline, backend=MsExcelDocumentBackend
),
@ -128,8 +143,8 @@ def _get_default_option(format: InputFormat) -> FormatOption:
InputFormat.XML_USPTO: FormatOption(
pipeline_cls=SimplePipeline, backend=PatentUsptoDocumentBackend
),
InputFormat.XML_PUBMED: FormatOption(
pipeline_cls=SimplePipeline, backend=PubMedDocumentBackend
InputFormat.XML_JATS: FormatOption(
pipeline_cls=SimplePipeline, backend=JatsDocumentBackend
),
InputFormat.IMAGE: FormatOption(
pipeline_cls=StandardPdfPipeline, backend=DoclingParseV2DocumentBackend
@ -184,6 +199,7 @@ class DocumentConverter:
raises_on_error: bool = True,
max_num_pages: int = sys.maxsize,
max_file_size: int = sys.maxsize,
page_range: PageRange = DEFAULT_PAGE_RANGE,
) -> ConversionResult:
all_res = self.convert_all(
source=[source],
@ -191,6 +207,7 @@ class DocumentConverter:
max_num_pages=max_num_pages,
max_file_size=max_file_size,
headers=headers,
page_range=page_range,
)
return next(all_res)
@ -202,10 +219,12 @@ class DocumentConverter:
raises_on_error: bool = True, # True: raises on first conversion error; False: does not raise on conv error
max_num_pages: int = sys.maxsize,
max_file_size: int = sys.maxsize,
page_range: PageRange = DEFAULT_PAGE_RANGE,
) -> Iterator[ConversionResult]:
limits = DocumentLimits(
max_num_pages=max_num_pages,
max_file_size=max_file_size,
page_range=page_range,
)
conv_input = _DocumentConversionInput(
path_or_stream_iterator=source, limits=limits, headers=headers

4
docling/exceptions.py
View File

@ -4,3 +4,7 @@ class BaseError(RuntimeError):
class ConversionError(BaseError):
pass
class OperationNotAllowed(BaseError):
pass

7
docling/models/base_model.py
View File

@ -1,11 +1,12 @@
from abc import ABC, abstractmethod
from typing import Any, Generic, Iterable, Optional
from docling_core.types.doc import BoundingBox, DoclingDocument, NodeItem, TextItem
from docling_core.types.doc import BoundingBox, DocItem, DoclingDocument, NodeItem
from typing_extensions import TypeVar
from docling.datamodel.base_models import ItemAndImageEnrichmentElement, Page
from docling.datamodel.document import ConversionResult
from docling.datamodel.settings import settings
class BasePageModel(ABC):
@ -21,6 +22,8 @@ EnrichElementT = TypeVar("EnrichElementT", default=NodeItem)
class GenericEnrichmentModel(ABC, Generic[EnrichElementT]):
elements_batch_size: int = settings.perf.elements_batch_size
@abstractmethod
def is_processable(self, doc: DoclingDocument, element: NodeItem) -> bool:
pass
@ -61,7 +64,7 @@ class BaseItemAndImageEnrichmentModel(
if not self.is_processable(doc=conv_res.document, element=element):
return None
assert isinstance(element, TextItem)
assert isinstance(element, DocItem)
element_prov = element.prov[0]
bbox = element_prov.bbox

24
docling/models/code_formula_model.py
View File

@ -2,6 +2,7 @@ import re
from pathlib import Path
from typing import Iterable, List, Literal, Optional, Tuple, Union
import numpy as np
from docling_core.types.doc import (
CodeItem,
DocItemLabel,
@ -61,13 +62,15 @@ class CodeFormulaModel(BaseItemAndImageEnrichmentModel):
Processes the given batch of elements and enriches them with predictions.
"""
_model_repo_folder = "ds4sd--CodeFormula"
elements_batch_size = 5
images_scale = 1.66 # = 120 dpi, aligned with training data resolution
expansion_factor = 0.03
def __init__(
self,
enabled: bool,
artifacts_path: Optional[Union[Path, str]],
artifacts_path: Optional[Path],
options: CodeFormulaModelOptions,
accelerator_options: AcceleratorOptions,
):
@ -96,29 +99,32 @@ class CodeFormulaModel(BaseItemAndImageEnrichmentModel):
)
if artifacts_path is None:
artifacts_path = self.download_models_hf()
artifacts_path = self.download_models()
else:
artifacts_path = Path(artifacts_path)
artifacts_path = artifacts_path / self._model_repo_folder
self.code_formula_model = CodeFormulaPredictor(
artifacts_path=artifacts_path,
artifacts_path=str(artifacts_path),
device=device,
num_threads=accelerator_options.num_threads,
)
@staticmethod
def download_models_hf(
local_dir: Optional[Path] = None, force: bool = False
def download_models(
local_dir: Optional[Path] = None,
force: bool = False,
progress: bool = False,
) -> Path:
from huggingface_hub import snapshot_download
from huggingface_hub.utils import disable_progress_bars
disable_progress_bars()
if not progress:
disable_progress_bars()
download_path = snapshot_download(
repo_id="ds4sd/CodeFormula",
force_download=force,
local_dir=local_dir,
revision="v1.0.0",
revision="v1.0.1",
)
return Path(download_path)
@ -226,7 +232,7 @@ class CodeFormulaModel(BaseItemAndImageEnrichmentModel):
return
labels: List[str] = []
images: List[Image.Image] = []
images: List[Union[Image.Image, np.ndarray]] = []
elements: List[TextItem] = []
for el in element_batch:
assert isinstance(el.item, TextItem)

19
docling/models/document_picture_classifier.py
View File

@ -1,6 +1,7 @@
from pathlib import Path
from typing import Iterable, List, Literal, Optional, Tuple, Union
import numpy as np
from docling_core.types.doc import (
DoclingDocument,
NodeItem,
@ -55,12 +56,13 @@ class DocumentPictureClassifier(BaseEnrichmentModel):
Processes a batch of elements and adds classification annotations.
"""
_model_repo_folder = "ds4sd--DocumentFigureClassifier"
images_scale = 2
def __init__(
self,
enabled: bool,
artifacts_path: Optional[Union[Path, str]],
artifacts_path: Optional[Path],
options: DocumentPictureClassifierOptions,
accelerator_options: AcceleratorOptions,
):
@ -88,24 +90,25 @@ class DocumentPictureClassifier(BaseEnrichmentModel):
)
if artifacts_path is None:
artifacts_path = self.download_models_hf()
artifacts_path = self.download_models()
else:
artifacts_path = Path(artifacts_path)
artifacts_path = artifacts_path / self._model_repo_folder
self.document_picture_classifier = DocumentFigureClassifierPredictor(
artifacts_path=artifacts_path,
artifacts_path=str(artifacts_path),
device=device,
num_threads=accelerator_options.num_threads,
)
@staticmethod
def download_models_hf(
local_dir: Optional[Path] = None, force: bool = False
def download_models(
local_dir: Optional[Path] = None, force: bool = False, progress: bool = False
) -> Path:
from huggingface_hub import snapshot_download
from huggingface_hub.utils import disable_progress_bars
disable_progress_bars()
if not progress:
disable_progress_bars()
download_path = snapshot_download(
repo_id="ds4sd/DocumentFigureClassifier",
force_download=force,
@ -159,7 +162,7 @@ class DocumentPictureClassifier(BaseEnrichmentModel):
yield element
return
images: List[Image.Image] = []
images: List[Union[Image.Image, np.ndarray]] = []
elements: List[PictureItem] = []
for el in element_batch:
assert isinstance(el, PictureItem)

328
docling/models/ds_glm_model.py
View File

@ -1,328 +0,0 @@
import copy
import random
from pathlib import Path
from typing import List, Union
from deepsearch_glm.andromeda_nlp import nlp_model
from docling_core.types.doc import BoundingBox, CoordOrigin, DoclingDocument
from docling_core.types.legacy_doc.base import BoundingBox as DsBoundingBox
from docling_core.types.legacy_doc.base import (
Figure,
PageDimensions,
PageReference,
Prov,
Ref,
)
from docling_core.types.legacy_doc.base import Table as DsSchemaTable
from docling_core.types.legacy_doc.base import TableCell
from docling_core.types.legacy_doc.document import BaseText
from docling_core.types.legacy_doc.document import (
CCSDocumentDescription as DsDocumentDescription,
)
from docling_core.types.legacy_doc.document import CCSFileInfoObject as DsFileInfoObject
from docling_core.types.legacy_doc.document import ExportedCCSDocument as DsDocument
from PIL import ImageDraw
from pydantic import BaseModel, ConfigDict, TypeAdapter
from docling.datamodel.base_models import (
Cluster,
ContainerElement,
FigureElement,
Table,
TextElement,
)
from docling.datamodel.document import ConversionResult, layout_label_to_ds_type
from docling.datamodel.settings import settings
from docling.utils.glm_utils import to_docling_document
from docling.utils.profiling import ProfilingScope, TimeRecorder
from docling.utils.utils import create_hash
class GlmOptions(BaseModel):
model_config = ConfigDict(protected_namespaces=())
model_names: str = "" # e.g. "language;term;reference"
class GlmModel:
def __init__(self, options: GlmOptions):
self.options = options
self.model = nlp_model(loglevel="error", text_ordering=True)
def _to_legacy_document(self, conv_res) -> DsDocument:
title = ""
desc: DsDocumentDescription = DsDocumentDescription(logs=[])
page_hashes = [
PageReference(
hash=create_hash(conv_res.input.document_hash + ":" + str(p.page_no)),
page=p.page_no + 1,
model="default",
)
for p in conv_res.pages
]
file_info = DsFileInfoObject(
filename=conv_res.input.file.name,
document_hash=conv_res.input.document_hash,
num_pages=conv_res.input.page_count,
page_hashes=page_hashes,
)
main_text: List[Union[Ref, BaseText]] = []
tables: List[DsSchemaTable] = []
figures: List[Figure] = []
page_no_to_page = {p.page_no: p for p in conv_res.pages}
for element in conv_res.assembled.elements:
# Convert bboxes to lower-left origin.
target_bbox = DsBoundingBox(
element.cluster.bbox.to_bottom_left_origin(
page_no_to_page[element.page_no].size.height
).as_tuple()
)
if isinstance(element, TextElement):
main_text.append(
BaseText(
text=element.text,
obj_type=layout_label_to_ds_type.get(element.label),
name=element.label,
prov=[
Prov(
bbox=target_bbox,
page=element.page_no + 1,
span=[0, len(element.text)],
)
],
)
)
elif isinstance(element, Table):
index = len(tables)
ref_str = f"#/tables/{index}"
main_text.append(
Ref(
name=element.label,
obj_type=layout_label_to_ds_type.get(element.label),
ref=ref_str,
),
)
# Initialise empty table data grid (only empty cells)
table_data = [
[
TableCell(
text="",
# bbox=[0,0,0,0],
spans=[[i, j]],
obj_type="body",
)
for j in range(element.num_cols)
]
for i in range(element.num_rows)
]
# Overwrite cells in table data for which there is actual cell content.
for cell in element.table_cells:
for i in range(
min(cell.start_row_offset_idx, element.num_rows),
min(cell.end_row_offset_idx, element.num_rows),
):
for j in range(
min(cell.start_col_offset_idx, element.num_cols),
min(cell.end_col_offset_idx, element.num_cols),
):
celltype = "body"
if cell.column_header:
celltype = "col_header"
elif cell.row_header:
celltype = "row_header"
elif cell.row_section:
celltype = "row_section"
def make_spans(cell):
for rspan in range(
min(cell.start_row_offset_idx, element.num_rows),
min(cell.end_row_offset_idx, element.num_rows),
):
for cspan in range(
min(
cell.start_col_offset_idx, element.num_cols
),
min(cell.end_col_offset_idx, element.num_cols),
):
yield [rspan, cspan]
spans = list(make_spans(cell))
if cell.bbox is not None:
bbox = cell.bbox.to_bottom_left_origin(
page_no_to_page[element.page_no].size.height
).as_tuple()
else:
bbox = None
table_data[i][j] = TableCell(
text=cell.text,
bbox=bbox,
# col=j,
# row=i,
spans=spans,
obj_type=celltype,
# col_span=[cell.start_col_offset_idx, cell.end_col_offset_idx],
# row_span=[cell.start_row_offset_idx, cell.end_row_offset_idx]
)
tables.append(
DsSchemaTable(
num_cols=element.num_cols,
num_rows=element.num_rows,
obj_type=layout_label_to_ds_type.get(element.label),
data=table_data,
prov=[
Prov(
bbox=target_bbox,
page=element.page_no + 1,
span=[0, 0],
)
],
)
)
elif isinstance(element, FigureElement):
index = len(figures)
ref_str = f"#/figures/{index}"
main_text.append(
Ref(
name=element.label,
obj_type=layout_label_to_ds_type.get(element.label),
ref=ref_str,
),
)
figures.append(
Figure(
prov=[
Prov(
bbox=target_bbox,
page=element.page_no + 1,
span=[0, 0],
)
],
obj_type=layout_label_to_ds_type.get(element.label),
payload={
"children": TypeAdapter(List[Cluster]).dump_python(
element.cluster.children
)
}, # hack to channel child clusters through GLM
)
)
elif isinstance(element, ContainerElement):
main_text.append(
BaseText(
text="",
payload={
"children": TypeAdapter(List[Cluster]).dump_python(
element.cluster.children
)
}, # hack to channel child clusters through GLM
obj_type=layout_label_to_ds_type.get(element.label),
name=element.label,
prov=[
Prov(
bbox=target_bbox,
page=element.page_no + 1,
span=[0, 0],
)
],
)
)
page_dimensions = [
PageDimensions(page=p.page_no + 1, height=p.size.height, width=p.size.width)
for p in conv_res.pages
if p.size is not None
]
ds_doc: DsDocument = DsDocument(
name=title,
description=desc,
file_info=file_info,
main_text=main_text,
tables=tables,
figures=figures,
page_dimensions=page_dimensions,
)
return ds_doc
def __call__(self, conv_res: ConversionResult) -> DoclingDocument:
with TimeRecorder(conv_res, "glm", scope=ProfilingScope.DOCUMENT):
ds_doc = self._to_legacy_document(conv_res)
ds_doc_dict = ds_doc.model_dump(by_alias=True, exclude_none=True)
glm_doc = self.model.apply_on_doc(ds_doc_dict)
docling_doc: DoclingDocument = to_docling_document(glm_doc) # Experimental
# DEBUG code:
def draw_clusters_and_cells(ds_document, page_no, show: bool = False):
clusters_to_draw = []
image = copy.deepcopy(conv_res.pages[page_no].image)
for ix, elem in enumerate(ds_document.main_text):
if isinstance(elem, BaseText):
prov = elem.prov[0] # type: ignore
elif isinstance(elem, Ref):
_, arr, index = elem.ref.split("/")
index = int(index) # type: ignore
if arr == "tables":
prov = ds_document.tables[index].prov[0]
elif arr == "figures":
prov = ds_document.pictures[index].prov[0]
else:
prov = None
if prov and prov.page == page_no:
clusters_to_draw.append(
Cluster(
id=ix,
label=elem.name,
bbox=BoundingBox.from_tuple(
coord=prov.bbox, # type: ignore
origin=CoordOrigin.BOTTOMLEFT,
).to_top_left_origin(conv_res.pages[page_no].size.height),
)
)
draw = ImageDraw.Draw(image)
for c in clusters_to_draw:
x0, y0, x1, y1 = c.bbox.as_tuple()
draw.rectangle([(x0, y0), (x1, y1)], outline="red")
draw.text((x0 + 2, y0 + 2), f"{c.id}:{c.label}", fill=(255, 0, 0, 255))
cell_color = (
random.randint(30, 140),
random.randint(30, 140),
random.randint(30, 140),
)
for tc in c.cells: # [:1]:
x0, y0, x1, y1 = tc.bbox.as_tuple()
draw.rectangle([(x0, y0), (x1, y1)], outline=cell_color)
if show:
image.show()
else:
out_path: Path = (
Path(settings.debug.debug_output_path)
/ f"debug_{conv_res.input.file.stem}"
)
out_path.mkdir(parents=True, exist_ok=True)
out_file = out_path / f"doc_page_{page_no:05}.png"
image.save(str(out_file), format="png")
# for item in ds_doc.page_dimensions:
# page_no = item.page
# draw_clusters_and_cells(ds_doc, page_no)
return docling_doc

53
docling/models/easyocr_model.py
View File

@ -1,9 +1,10 @@
import logging
import warnings
from typing import Iterable
import zipfile
from pathlib import Path
from typing import Iterable, List, Optional
import numpy
import torch
from docling_core.types.doc import BoundingBox, CoordOrigin
from docling.datamodel.base_models import Cell, OcrCell, Page
@ -17,14 +18,18 @@ from docling.datamodel.settings import settings
from docling.models.base_ocr_model import BaseOcrModel
from docling.utils.accelerator_utils import decide_device
from docling.utils.profiling import TimeRecorder
from docling.utils.utils import download_url_with_progress
_log = logging.getLogger(__name__)
class EasyOcrModel(BaseOcrModel):
_model_repo_folder = "EasyOcr"
def __init__(
self,
enabled: bool,
artifacts_path: Optional[Path],
options: EasyOcrOptions,
accelerator_options: AcceleratorOptions,
):
@ -62,15 +67,55 @@ class EasyOcrModel(BaseOcrModel):
)
use_gpu = self.options.use_gpu
download_enabled = self.options.download_enabled
model_storage_directory = self.options.model_storage_directory
if artifacts_path is not None and model_storage_directory is None:
download_enabled = False
model_storage_directory = str(artifacts_path / self._model_repo_folder)
self.reader = easyocr.Reader(
lang_list=self.options.lang,
gpu=use_gpu,
model_storage_directory=self.options.model_storage_directory,
model_storage_directory=model_storage_directory,
recog_network=self.options.recog_network,
download_enabled=self.options.download_enabled,
download_enabled=download_enabled,
verbose=False,
)
@staticmethod
def download_models(
detection_models: List[str] = ["craft"],
recognition_models: List[str] = ["english_g2", "latin_g2"],
local_dir: Optional[Path] = None,
force: bool = False,
progress: bool = False,
) -> Path:
# Models are located in https://github.com/JaidedAI/EasyOCR/blob/master/easyocr/config.py
from easyocr.config import detection_models as det_models_dict
from easyocr.config import recognition_models as rec_models_dict
if local_dir is None:
local_dir = settings.cache_dir / "models" / EasyOcrModel._model_repo_folder
local_dir.mkdir(parents=True, exist_ok=True)
# Collect models to download
download_list = []
for model_name in detection_models:
if model_name in det_models_dict:
download_list.append(det_models_dict[model_name])
for model_name in recognition_models:
if model_name in rec_models_dict["gen2"]:
download_list.append(rec_models_dict["gen2"][model_name])
# Download models
for model_details in download_list:
buf = download_url_with_progress(model_details["url"], progress=progress)
with zipfile.ZipFile(buf, "r") as zip_ref:
zip_ref.extractall(local_dir)
return local_dir
def __call__(
self, conv_res: ConversionResult, page_batch: Iterable[Page]
) -> Iterable[Page]:

180
docling/models/hf_vlm_model.py Normal file
View File

@ -0,0 +1,180 @@
import logging
import time
from pathlib import Path
from typing import Iterable, List, Optional
from docling.datamodel.base_models import Page, VlmPrediction
from docling.datamodel.document import ConversionResult
from docling.datamodel.pipeline_options import (
AcceleratorDevice,
AcceleratorOptions,
HuggingFaceVlmOptions,
)
from docling.datamodel.settings import settings
from docling.models.base_model import BasePageModel
from docling.utils.accelerator_utils import decide_device
from docling.utils.profiling import TimeRecorder
_log = logging.getLogger(__name__)
class HuggingFaceVlmModel(BasePageModel):
def __init__(
self,
enabled: bool,
artifacts_path: Optional[Path],
accelerator_options: AcceleratorOptions,
vlm_options: HuggingFaceVlmOptions,
):
self.enabled = enabled
self.vlm_options = vlm_options
if self.enabled:
import torch
from transformers import ( # type: ignore
AutoModelForVision2Seq,
AutoProcessor,
BitsAndBytesConfig,
)
device = decide_device(accelerator_options.device)
self.device = device
_log.debug("Available device for HuggingFace VLM: {}".format(device))
repo_cache_folder = vlm_options.repo_id.replace("/", "--")
# PARAMETERS:
if artifacts_path is None:
artifacts_path = self.download_models(self.vlm_options.repo_id)
elif (artifacts_path / repo_cache_folder).exists():
artifacts_path = artifacts_path / repo_cache_folder
self.param_question = vlm_options.prompt # "Perform Layout Analysis."
self.param_quantization_config = BitsAndBytesConfig(
load_in_8bit=vlm_options.load_in_8bit, # True,
llm_int8_threshold=vlm_options.llm_int8_threshold, # 6.0
)
self.param_quantized = vlm_options.quantized # False
self.processor = AutoProcessor.from_pretrained(artifacts_path)
if not self.param_quantized:
self.vlm_model = AutoModelForVision2Seq.from_pretrained(
artifacts_path,
device_map=device,
torch_dtype=torch.bfloat16,
_attn_implementation=(
"flash_attention_2"
if self.device.startswith("cuda")
and accelerator_options.cuda_use_flash_attention2
else "eager"
),
) # .to(self.device)
else:
self.vlm_model = AutoModelForVision2Seq.from_pretrained(
artifacts_path,
device_map=device,
torch_dtype="auto",
quantization_config=self.param_quantization_config,
_attn_implementation=(
"flash_attention_2"
if self.device.startswith("cuda")
and accelerator_options.cuda_use_flash_attention2
else "eager"
),
) # .to(self.device)
@staticmethod
def download_models(
repo_id: str,
local_dir: Optional[Path] = None,
force: bool = False,
progress: bool = False,
) -> Path:
from huggingface_hub import snapshot_download
from huggingface_hub.utils import disable_progress_bars
if not progress:
disable_progress_bars()
download_path = snapshot_download(
repo_id=repo_id,
force_download=force,
local_dir=local_dir,
# revision="v0.0.1",
)
return Path(download_path)
def __call__(
self, conv_res: ConversionResult, page_batch: Iterable[Page]
) -> Iterable[Page]:
for page in page_batch:
assert page._backend is not None
if not page._backend.is_valid():
yield page
else:
with TimeRecorder(conv_res, "vlm"):
assert page.size is not None
hi_res_image = page.get_image(scale=2.0) # 144dpi
# hi_res_image = page.get_image(scale=1.0) # 72dpi
if hi_res_image is not None:
im_width, im_height = hi_res_image.size
# populate page_tags with predicted doc tags
page_tags = ""
if hi_res_image:
if hi_res_image.mode != "RGB":
hi_res_image = hi_res_image.convert("RGB")
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": "This is a page from a document.",
},
{"type": "image"},
{"type": "text", "text": self.param_question},
],
}
]
prompt = self.processor.apply_chat_template(
messages, add_generation_prompt=False
)
inputs = self.processor(
text=prompt, images=[hi_res_image], return_tensors="pt"
)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
start_time = time.time()
# Call model to generate:
generated_ids = self.vlm_model.generate(
**inputs, max_new_tokens=4096, use_cache=True
)
generation_time = time.time() - start_time
generated_texts = self.processor.batch_decode(
generated_ids[:, inputs["input_ids"].shape[1] :],
skip_special_tokens=False,
)[0]
num_tokens = len(generated_ids[0])
page_tags = generated_texts
# inference_time = time.time() - start_time
# tokens_per_second = num_tokens / generation_time
# print("")
# print(f"Page Inference Time: {inference_time:.2f} seconds")
# print(f"Total tokens on page: {num_tokens:.2f}")
# print(f"Tokens/sec: {tokens_per_second:.2f}")
# print("")
page.predictions.vlm_response = VlmPrediction(text=page_tags)
yield page

52
docling/models/layout_model.py
View File

@ -1,7 +1,8 @@
import copy
import logging
import warnings
from pathlib import Path
from typing import Iterable
from typing import Iterable, Optional, Union
from docling_core.types.doc import DocItemLabel
from docling_ibm_models.layoutmodel.layout_predictor import LayoutPredictor
@ -21,6 +22,8 @@ _log = logging.getLogger(__name__)
class LayoutModel(BasePageModel):
_model_repo_folder = "ds4sd--docling-models"
_model_path = "model_artifacts/layout"
TEXT_ELEM_LABELS = [
DocItemLabel.TEXT,
@ -42,15 +45,56 @@ class LayoutModel(BasePageModel):
FORMULA_LABEL = DocItemLabel.FORMULA
CONTAINER_LABELS = [DocItemLabel.FORM, DocItemLabel.KEY_VALUE_REGION]
def __init__(self, artifacts_path: Path, accelerator_options: AcceleratorOptions):
def __init__(
self, artifacts_path: Optional[Path], accelerator_options: AcceleratorOptions
):
device = decide_device(accelerator_options.device)
if artifacts_path is None:
artifacts_path = self.download_models() / self._model_path
else:
# will become the default in the future
if (artifacts_path / self._model_repo_folder).exists():
artifacts_path = (
artifacts_path / self._model_repo_folder / self._model_path
)
elif (artifacts_path / self._model_path).exists():
warnings.warn(
"The usage of artifacts_path containing directly "
f"{self._model_path} is deprecated. Please point "
"the artifacts_path to the parent containing "
f"the {self._model_repo_folder} folder.",
DeprecationWarning,
stacklevel=3,
)
artifacts_path = artifacts_path / self._model_path
self.layout_predictor = LayoutPredictor(
artifact_path=str(artifacts_path),
device=device,
num_threads=accelerator_options.num_threads,
)
@staticmethod
def download_models(
local_dir: Optional[Path] = None,
force: bool = False,
progress: bool = False,
) -> Path:
from huggingface_hub import snapshot_download
from huggingface_hub.utils import disable_progress_bars
if not progress:
disable_progress_bars()
download_path = snapshot_download(
repo_id="ds4sd/docling-models",
force_download=force,
local_dir=local_dir,
revision="v2.1.0",
)
return Path(download_path)
def draw_clusters_and_cells_side_by_side(
self, conv_res, page, clusters, mode_prefix: str, show: bool = False
):
@ -106,10 +150,12 @@ class LayoutModel(BasePageModel):
else:
with TimeRecorder(conv_res, "layout"):
assert page.size is not None
page_image = page.get_image(scale=1.0)
assert page_image is not None
clusters = []
for ix, pred_item in enumerate(
self.layout_predictor.predict(page.get_image(scale=1.0))
self.layout_predictor.predict(page_image)
):
label = DocItemLabel(
pred_item["label"]

8
docling/models/page_assemble_model.py
View File

@ -52,6 +52,14 @@ class PageAssembleModel(BasePageModel):
sanitized_text = "".join(lines)
# Text normalization
sanitized_text = sanitized_text.replace("⁄", "/")
sanitized_text = sanitized_text.replace("’", "'")
sanitized_text = sanitized_text.replace("‘", "'")
sanitized_text = sanitized_text.replace("“", '"')
sanitized_text = sanitized_text.replace("”", '"')
sanitized_text = sanitized_text.replace("•", "·")
return sanitized_text.strip() # Strip any leading or trailing whitespace
def __call__(

108
docling/models/picture_description_api_model.py Normal file
View File

@ -0,0 +1,108 @@
import base64
import io
import logging
from typing import Iterable, List, Optional
import requests
from PIL import Image
from pydantic import BaseModel, ConfigDict
from docling.datamodel.pipeline_options import PictureDescriptionApiOptions
from docling.exceptions import OperationNotAllowed
from docling.models.picture_description_base_model import PictureDescriptionBaseModel
_log = logging.getLogger(__name__)
class ChatMessage(BaseModel):
role: str
content: str
class ResponseChoice(BaseModel):
index: int
message: ChatMessage
finish_reason: str
class ResponseUsage(BaseModel):
prompt_tokens: int
completion_tokens: int
total_tokens: int
class ApiResponse(BaseModel):
model_config = ConfigDict(
protected_namespaces=(),
)
id: str
model: Optional[str] = None # returned by openai
choices: List[ResponseChoice]
created: int
usage: ResponseUsage
class PictureDescriptionApiModel(PictureDescriptionBaseModel):
# elements_batch_size = 4
def __init__(
self,
enabled: bool,
enable_remote_services: bool,
options: PictureDescriptionApiOptions,
):
super().__init__(enabled=enabled, options=options)
self.options: PictureDescriptionApiOptions
if self.enabled:
if not enable_remote_services:
raise OperationNotAllowed(
"Connections to remote services is only allowed when set explicitly. "
"pipeline_options.enable_remote_services=True."
)
def _annotate_images(self, images: Iterable[Image.Image]) -> Iterable[str]:
# Note: technically we could make a batch request here,
# but not all APIs will allow for it. For example, vllm won't allow more than 1.
for image in images:
img_io = io.BytesIO()
image.save(img_io, "PNG")
image_base64 = base64.b64encode(img_io.getvalue()).decode("utf-8")
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": self.options.prompt,
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/png;base64,{image_base64}"
},
},
],
}
]
payload = {
"messages": messages,
**self.options.params,
}
r = requests.post(
str(self.options.url),
headers=self.options.headers,
json=payload,
timeout=self.options.timeout,
)
if not r.ok:
_log.error(f"Error calling the API. Reponse was {r.text}")
r.raise_for_status()
api_resp = ApiResponse.model_validate_json(r.text)
generated_text = api_resp.choices[0].message.content.strip()
yield generated_text

64
docling/models/picture_description_base_model.py Normal file
View File

@ -0,0 +1,64 @@
import logging
from pathlib import Path
from typing import Any, Iterable, List, Optional, Union
from docling_core.types.doc import (
DoclingDocument,
NodeItem,
PictureClassificationClass,
PictureItem,
)
from docling_core.types.doc.document import ( # TODO: move import to docling_core.types.doc
PictureDescriptionData,
)
from PIL import Image
from docling.datamodel.pipeline_options import PictureDescriptionBaseOptions
from docling.models.base_model import (
BaseItemAndImageEnrichmentModel,
ItemAndImageEnrichmentElement,
)
class PictureDescriptionBaseModel(BaseItemAndImageEnrichmentModel):
images_scale: float = 2.0
def __init__(
self,
enabled: bool,
options: PictureDescriptionBaseOptions,
):
self.enabled = enabled
self.options = options
self.provenance = "not-implemented"
def is_processable(self, doc: DoclingDocument, element: NodeItem) -> bool:
return self.enabled and isinstance(element, PictureItem)
def _annotate_images(self, images: Iterable[Image.Image]) -> Iterable[str]:
raise NotImplementedError
def __call__(
self,
doc: DoclingDocument,
element_batch: Iterable[ItemAndImageEnrichmentElement],
) -> Iterable[NodeItem]:
if not self.enabled:
for element in element_batch:
yield element.item
return
images: List[Image.Image] = []
elements: List[PictureItem] = []
for el in element_batch:
assert isinstance(el.item, PictureItem)
elements.append(el.item)
images.append(el.image)
outputs = self._annotate_images(images)
for item, output in zip(elements, outputs):
item.annotations.append(
PictureDescriptionData(text=output, provenance=self.provenance)
)
yield item

109
docling/models/picture_description_vlm_model.py Normal file
View File

@ -0,0 +1,109 @@
from pathlib import Path
from typing import Iterable, Optional, Union
from PIL import Image
from docling.datamodel.pipeline_options import (
AcceleratorOptions,
PictureDescriptionVlmOptions,
)
from docling.models.picture_description_base_model import PictureDescriptionBaseModel
from docling.utils.accelerator_utils import decide_device
class PictureDescriptionVlmModel(PictureDescriptionBaseModel):
def __init__(
self,
enabled: bool,
artifacts_path: Optional[Union[Path, str]],
options: PictureDescriptionVlmOptions,
accelerator_options: AcceleratorOptions,
):
super().__init__(enabled=enabled, options=options)
self.options: PictureDescriptionVlmOptions
if self.enabled:
if artifacts_path is None:
artifacts_path = self.download_models(repo_id=self.options.repo_id)
else:
artifacts_path = Path(artifacts_path) / self.options.repo_cache_folder
self.device = decide_device(accelerator_options.device)
try:
import torch
from transformers import AutoModelForVision2Seq, AutoProcessor
except ImportError:
raise ImportError(
"transformers >=4.46 is not installed. Please install Docling with the required extras `pip install docling[vlm]`."
)
# Initialize processor and model
self.processor = AutoProcessor.from_pretrained(artifacts_path)
self.model = AutoModelForVision2Seq.from_pretrained(
artifacts_path,
torch_dtype=torch.bfloat16,
_attn_implementation=(
"flash_attention_2" if self.device.startswith("cuda") else "eager"
),
).to(self.device)
self.provenance = f"{self.options.repo_id}"
@staticmethod
def download_models(
repo_id: str,
local_dir: Optional[Path] = None,
force: bool = False,
progress: bool = False,
) -> Path:
from huggingface_hub import snapshot_download
from huggingface_hub.utils import disable_progress_bars
if not progress:
disable_progress_bars()
download_path = snapshot_download(
repo_id=repo_id,
force_download=force,
local_dir=local_dir,
)
return Path(download_path)
def _annotate_images(self, images: Iterable[Image.Image]) -> Iterable[str]:
from transformers import GenerationConfig
# Create input messages
messages = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": self.options.prompt},
],
},
]
# TODO: do batch generation
for image in images:
# Prepare inputs
prompt = self.processor.apply_chat_template(
messages, add_generation_prompt=True
)
inputs = self.processor(text=prompt, images=[image], return_tensors="pt")
inputs = inputs.to(self.device)
# Generate outputs
generated_ids = self.model.generate(
**inputs,
generation_config=GenerationConfig(**self.options.generation_config),
)
generated_texts = self.processor.batch_decode(
generated_ids[:, inputs["input_ids"].shape[1] :],
skip_special_tokens=True,
)
yield generated_texts[0].strip()

389
docling/models/readingorder_model.py Normal file
View File

@ -0,0 +1,389 @@
import copy
import random
from pathlib import Path
from typing import Dict, List
from docling_core.types.doc import (
BoundingBox,
CoordOrigin,
DocItem,
DocItemLabel,
DoclingDocument,
DocumentOrigin,
GroupLabel,
NodeItem,
ProvenanceItem,
RefItem,
TableData,
)
from docling_core.types.doc.document import ContentLayer
from docling_core.types.legacy_doc.base import Ref
from docling_core.types.legacy_doc.document import BaseText
from docling_ibm_models.reading_order.reading_order_rb import (
PageElement as ReadingOrderPageElement,
)
from docling_ibm_models.reading_order.reading_order_rb import ReadingOrderPredictor
from PIL import ImageDraw
from pydantic import BaseModel, ConfigDict
from docling.datamodel.base_models import (
BasePageElement,
Cluster,
ContainerElement,
FigureElement,
Table,
TextElement,
)
from docling.datamodel.document import ConversionResult
from docling.datamodel.settings import settings
from docling.utils.profiling import ProfilingScope, TimeRecorder
class ReadingOrderOptions(BaseModel):
model_config = ConfigDict(protected_namespaces=())
model_names: str = "" # e.g. "language;term;reference"
class ReadingOrderModel:
def __init__(self, options: ReadingOrderOptions):
self.options = options
self.ro_model = ReadingOrderPredictor()
def _assembled_to_readingorder_elements(
self, conv_res: ConversionResult
) -> List[ReadingOrderPageElement]:
elements: List[ReadingOrderPageElement] = []
page_no_to_pages = {p.page_no: p for p in conv_res.pages}
for element in conv_res.assembled.elements:
page_height = page_no_to_pages[element.page_no].size.height # type: ignore
bbox = element.cluster.bbox.to_bottom_left_origin(page_height)
text = element.text or ""
elements.append(
ReadingOrderPageElement(
cid=len(elements),
ref=RefItem(cref=f"#/{element.page_no}/{element.cluster.id}"),
text=text,
page_no=element.page_no,
page_size=page_no_to_pages[element.page_no].size,
label=element.label,
l=bbox.l,
r=bbox.r,
b=bbox.b,
t=bbox.t,
coord_origin=bbox.coord_origin,
)
)
return elements
def _add_child_elements(
self, element: BasePageElement, doc_item: NodeItem, doc: DoclingDocument
):
child: Cluster
for child in element.cluster.children:
c_label = child.label
c_bbox = child.bbox.to_bottom_left_origin(
doc.pages[element.page_no + 1].size.height
)
c_text = " ".join(
[
cell.text.replace("\x02", "-").strip()
for cell in child.cells
if len(cell.text.strip()) > 0
]
)
c_prov = ProvenanceItem(
page_no=element.page_no + 1, charspan=(0, len(c_text)), bbox=c_bbox
)
if c_label == DocItemLabel.LIST_ITEM:
# TODO: Infer if this is a numbered or a bullet list item
doc.add_list_item(parent=doc_item, text=c_text, prov=c_prov)
elif c_label == DocItemLabel.SECTION_HEADER:
doc.add_heading(parent=doc_item, text=c_text, prov=c_prov)
else:
doc.add_text(parent=doc_item, label=c_label, text=c_text, prov=c_prov)
def _readingorder_elements_to_docling_doc(
self,
conv_res: ConversionResult,
ro_elements: List[ReadingOrderPageElement],
el_to_captions_mapping: Dict[int, List[int]],
el_to_footnotes_mapping: Dict[int, List[int]],
el_merges_mapping: Dict[int, List[int]],
) -> DoclingDocument:
id_to_elem = {
RefItem(cref=f"#/{elem.page_no}/{elem.cluster.id}").cref: elem
for elem in conv_res.assembled.elements
}
cid_to_rels = {rel.cid: rel for rel in ro_elements}
origin = DocumentOrigin(
mimetype="application/pdf",
filename=conv_res.input.file.name,
binary_hash=conv_res.input.document_hash,
)
doc_name = Path(origin.filename).stem
out_doc: DoclingDocument = DoclingDocument(name=doc_name, origin=origin)
for page in conv_res.pages:
page_no = page.page_no + 1
size = page.size
assert size is not None
out_doc.add_page(page_no=page_no, size=size)
current_list = None
skippable_cids = {
cid
for mapping in (
el_to_captions_mapping,
el_to_footnotes_mapping,
el_merges_mapping,
)
for lst in mapping.values()
for cid in lst
}
page_no_to_pages = {p.page_no: p for p in conv_res.pages}
for rel in ro_elements:
if rel.cid in skippable_cids:
continue
element = id_to_elem[rel.ref.cref]
page_height = page_no_to_pages[element.page_no].size.height # type: ignore
if isinstance(element, TextElement):
if element.label == DocItemLabel.CODE:
cap_text = element.text
prov = ProvenanceItem(
page_no=element.page_no + 1,
charspan=(0, len(cap_text)),
bbox=element.cluster.bbox.to_bottom_left_origin(page_height),
)
code_item = out_doc.add_code(text=cap_text, prov=prov)
if rel.cid in el_to_captions_mapping.keys():
for caption_cid in el_to_captions_mapping[rel.cid]:
caption_elem = id_to_elem[cid_to_rels[caption_cid].ref.cref]
new_cap_item = self._add_caption_or_footnote(
caption_elem, out_doc, code_item, page_height
)
code_item.captions.append(new_cap_item.get_ref())
if rel.cid in el_to_footnotes_mapping.keys():
for footnote_cid in el_to_footnotes_mapping[rel.cid]:
footnote_elem = id_to_elem[
cid_to_rels[footnote_cid].ref.cref
]
new_footnote_item = self._add_caption_or_footnote(
footnote_elem, out_doc, code_item, page_height
)
code_item.footnotes.append(new_footnote_item.get_ref())
else:
new_item, current_list = self._handle_text_element(
element, out_doc, current_list, page_height
)
if rel.cid in el_merges_mapping.keys():
for merged_cid in el_merges_mapping[rel.cid]:
merged_elem = id_to_elem[cid_to_rels[merged_cid].ref.cref]
self._merge_elements(
element, merged_elem, new_item, page_height
)
elif isinstance(element, Table):
tbl_data = TableData(
num_rows=element.num_rows,
num_cols=element.num_cols,
table_cells=element.table_cells,
)
prov = ProvenanceItem(
page_no=element.page_no + 1,
charspan=(0, 0),
bbox=element.cluster.bbox.to_bottom_left_origin(page_height),
)
tbl = out_doc.add_table(
data=tbl_data, prov=prov, label=element.cluster.label
)
if rel.cid in el_to_captions_mapping.keys():
for caption_cid in el_to_captions_mapping[rel.cid]:
caption_elem = id_to_elem[cid_to_rels[caption_cid].ref.cref]
new_cap_item = self._add_caption_or_footnote(
caption_elem, out_doc, tbl, page_height
)
tbl.captions.append(new_cap_item.get_ref())
if rel.cid in el_to_footnotes_mapping.keys():
for footnote_cid in el_to_footnotes_mapping[rel.cid]:
footnote_elem = id_to_elem[cid_to_rels[footnote_cid].ref.cref]
new_footnote_item = self._add_caption_or_footnote(
footnote_elem, out_doc, tbl, page_height
)
tbl.footnotes.append(new_footnote_item.get_ref())
# TODO: Consider adding children of Table.
elif isinstance(element, FigureElement):
cap_text = ""
prov = ProvenanceItem(
page_no=element.page_no + 1,
charspan=(0, len(cap_text)),
bbox=element.cluster.bbox.to_bottom_left_origin(page_height),
)
pic = out_doc.add_picture(prov=prov)
if rel.cid in el_to_captions_mapping.keys():
for caption_cid in el_to_captions_mapping[rel.cid]:
caption_elem = id_to_elem[cid_to_rels[caption_cid].ref.cref]
new_cap_item = self._add_caption_or_footnote(
caption_elem, out_doc, pic, page_height
)
pic.captions.append(new_cap_item.get_ref())
if rel.cid in el_to_footnotes_mapping.keys():
for footnote_cid in el_to_footnotes_mapping[rel.cid]:
footnote_elem = id_to_elem[cid_to_rels[footnote_cid].ref.cref]
new_footnote_item = self._add_caption_or_footnote(
footnote_elem, out_doc, pic, page_height
)
pic.footnotes.append(new_footnote_item.get_ref())
self._add_child_elements(element, pic, out_doc)
elif isinstance(element, ContainerElement): # Form, KV region
label = element.label
group_label = GroupLabel.UNSPECIFIED
if label == DocItemLabel.FORM:
group_label = GroupLabel.FORM_AREA
elif label == DocItemLabel.KEY_VALUE_REGION:
group_label = GroupLabel.KEY_VALUE_AREA
container_el = out_doc.add_group(label=group_label)
self._add_child_elements(element, container_el, out_doc)
return out_doc
def _add_caption_or_footnote(self, elem, out_doc, parent, page_height):
assert isinstance(elem, TextElement)
text = elem.text
prov = ProvenanceItem(
page_no=elem.page_no + 1,
charspan=(0, len(text)),
bbox=elem.cluster.bbox.to_bottom_left_origin(page_height),
)
new_item = out_doc.add_text(
label=elem.label, text=text, prov=prov, parent=parent
)
return new_item
def _handle_text_element(self, element, out_doc, current_list, page_height):
cap_text = element.text
prov = ProvenanceItem(
page_no=element.page_no + 1,
charspan=(0, len(cap_text)),
bbox=element.cluster.bbox.to_bottom_left_origin(page_height),
)
label = element.label
if label == DocItemLabel.LIST_ITEM:
if current_list is None:
current_list = out_doc.add_group(label=GroupLabel.LIST, name="list")
# TODO: Infer if this is a numbered or a bullet list item
new_item = out_doc.add_list_item(
text=cap_text, enumerated=False, prov=prov, parent=current_list
)
elif label == DocItemLabel.SECTION_HEADER:
current_list = None
new_item = out_doc.add_heading(text=cap_text, prov=prov)
elif label == DocItemLabel.FORMULA:
current_list = None
new_item = out_doc.add_text(
label=DocItemLabel.FORMULA, text="", orig=cap_text, prov=prov
)
else:
current_list = None
content_layer = ContentLayer.BODY
if element.label in [DocItemLabel.PAGE_HEADER, DocItemLabel.PAGE_FOOTER]:
content_layer = ContentLayer.FURNITURE
new_item = out_doc.add_text(
label=element.label,
text=cap_text,
prov=prov,
content_layer=content_layer,
)
return new_item, current_list
def _merge_elements(self, element, merged_elem, new_item, page_height):
assert isinstance(
merged_elem, type(element)
), "Merged element must be of same type as element."
assert (
merged_elem.label == new_item.label
), "Labels of merged elements must match."
prov = ProvenanceItem(
page_no=element.page_no + 1,
charspan=(
len(new_item.text) + 1,
len(new_item.text) + 1 + len(merged_elem.text),
),
bbox=element.cluster.bbox.to_bottom_left_origin(page_height),
)
new_item.text += f" {merged_elem.text}"
new_item.orig += f" {merged_elem.text}" # TODO: This is incomplete, we don't have the `orig` field of the merged element.
new_item.prov.append(prov)
def __call__(self, conv_res: ConversionResult) -> DoclingDocument:
with TimeRecorder(conv_res, "glm", scope=ProfilingScope.DOCUMENT):
page_elements = self._assembled_to_readingorder_elements(conv_res)
# Apply reading order
sorted_elements = self.ro_model.predict_reading_order(
page_elements=page_elements
)
el_to_captions_mapping = self.ro_model.predict_to_captions(
sorted_elements=sorted_elements
)
el_to_footnotes_mapping = self.ro_model.predict_to_footnotes(
sorted_elements=sorted_elements
)
el_merges_mapping = self.ro_model.predict_merges(
sorted_elements=sorted_elements
)
docling_doc: DoclingDocument = self._readingorder_elements_to_docling_doc(
conv_res,
sorted_elements,
el_to_captions_mapping,
el_to_footnotes_mapping,
el_merges_mapping,
)
return docling_doc

60
docling/models/table_structure_model.py
View File

@ -1,6 +1,7 @@
import copy
import warnings
from pathlib import Path
from typing import Iterable
from typing import Iterable, Optional, Union
import numpy
from docling_core.types.doc import BoundingBox, DocItemLabel, TableCell
@ -22,10 +23,13 @@ from docling.utils.profiling import TimeRecorder
class TableStructureModel(BasePageModel):
_model_repo_folder = "ds4sd--docling-models"
_model_path = "model_artifacts/tableformer"
def __init__(
self,
enabled: bool,
artifacts_path: Path,
artifacts_path: Optional[Path],
options: TableStructureOptions,
accelerator_options: AcceleratorOptions,
):
@ -35,6 +39,26 @@ class TableStructureModel(BasePageModel):
self.enabled = enabled
if self.enabled:
if artifacts_path is None:
artifacts_path = self.download_models() / self._model_path
else:
# will become the default in the future
if (artifacts_path / self._model_repo_folder).exists():
artifacts_path = (
artifacts_path / self._model_repo_folder / self._model_path
)
elif (artifacts_path / self._model_path).exists():
warnings.warn(
"The usage of artifacts_path containing directly "
f"{self._model_path} is deprecated. Please point "
"the artifacts_path to the parent containing "
f"the {self._model_repo_folder} folder.",
DeprecationWarning,
stacklevel=3,
)
artifacts_path = artifacts_path / self._model_path
if self.mode == TableFormerMode.ACCURATE:
artifacts_path = artifacts_path / "accurate"
else:
@ -58,6 +82,24 @@ class TableStructureModel(BasePageModel):
)
self.scale = 2.0 # Scale up table input images to 144 dpi
@staticmethod
def download_models(
local_dir: Optional[Path] = None, force: bool = False, progress: bool = False
) -> Path:
from huggingface_hub import snapshot_download
from huggingface_hub.utils import disable_progress_bars
if not progress:
disable_progress_bars()
download_path = snapshot_download(
repo_id="ds4sd/docling-models",
force_download=force,
local_dir=local_dir,
revision="v2.1.0",
)
return Path(download_path)
def draw_table_and_cells(
self,
conv_res: ConversionResult,
@ -209,12 +251,16 @@ class TableStructureModel(BasePageModel):
tc.bbox = tc.bbox.scaled(1 / self.scale)
table_cells.append(tc)
assert "predict_details" in table_out
# Retrieving cols/rows, after post processing:
num_rows = table_out["predict_details"]["num_rows"]
num_cols = table_out["predict_details"]["num_cols"]
otsl_seq = table_out["predict_details"]["prediction"][
"rs_seq"
]
num_rows = table_out["predict_details"].get("num_rows", 0)
num_cols = table_out["predict_details"].get("num_cols", 0)
otsl_seq = (
table_out["predict_details"]
.get("prediction", {})
.get("rs_seq", [])
)
tbl = Table(
otsl_seq=otsl_seq,

4
docling/models/tesseract_ocr_cli_model.py
View File

@ -114,7 +114,9 @@ class TesseractOcrCliModel(BaseOcrModel):
# _log.info("df: ", df.head())
# Filter rows that contain actual text (ignore header or empty rows)
df_filtered = df[df["text"].notnull() & (df["text"].str.strip() != "")]
df_filtered = df[
df["text"].notnull() & (df["text"].apply(str).str.strip() != "")
]
return df_filtered

3
docling/models/tesseract_ocr_model.py
View File

@ -22,6 +22,7 @@ class TesseractOcrModel(BaseOcrModel):
self.scale = 3 # multiplier for 72 dpi == 216 dpi.
self.reader = None
self.osd_reader = None
self.script_readers: dict[str, tesserocr.PyTessBaseAPI] = {}
if self.enabled:
install_errmsg = (
@ -57,8 +58,6 @@ class TesseractOcrModel(BaseOcrModel):
_log.debug("Initializing TesserOCR: %s", tesseract_version)
lang = "+".join(self.options.lang)
self.script_readers: dict[str, tesserocr.PyTessBaseAPI] = {}
if any([l.startswith("script/") for l in self._tesserocr_languages]):
self.script_prefix = "script/"
else:

6
docling/pipeline/base_pipeline.py
View File

@ -79,7 +79,7 @@ class BasePipeline(ABC):
for model in self.enrichment_pipe:
for element_batch in chunkify(
_prepare_elements(conv_res, model),
settings.perf.elements_batch_size,
model.elements_batch_size,
):
for element in model(
doc=conv_res.document, element_batch=element_batch
@ -141,7 +141,9 @@ class PaginatedPipeline(BasePipeline): # TODO this is a bad name.
with TimeRecorder(conv_res, "doc_build", scope=ProfilingScope.DOCUMENT):
for i in range(0, conv_res.input.page_count):
conv_res.pages.append(Page(page_no=i))
start_page, end_page = conv_res.input.limits.page_range
if (start_page - 1) <= i <= (end_page - 1):
conv_res.pages.append(Page(page_no=i))
try:
# Iterate batches of pages (page_batch_size) in the doc

104
docling/pipeline/standard_pdf_pipeline.py
View File

@ -1,5 +1,6 @@
import logging
import sys
import warnings
from pathlib import Path
from typing import Optional
@ -13,17 +14,19 @@ from docling.datamodel.pipeline_options import (
EasyOcrOptions,
OcrMacOptions,
PdfPipelineOptions,
PictureDescriptionApiOptions,
PictureDescriptionVlmOptions,
RapidOcrOptions,
TesseractCliOcrOptions,
TesseractOcrOptions,
)
from docling.datamodel.settings import settings
from docling.models.base_ocr_model import BaseOcrModel
from docling.models.code_formula_model import CodeFormulaModel, CodeFormulaModelOptions
from docling.models.document_picture_classifier import (
DocumentPictureClassifier,
DocumentPictureClassifierOptions,
)
from docling.models.ds_glm_model import GlmModel, GlmOptions
from docling.models.easyocr_model import EasyOcrModel
from docling.models.layout_model import LayoutModel
from docling.models.ocr_mac_model import OcrMacModel
@ -32,28 +35,40 @@ from docling.models.page_preprocessing_model import (
PagePreprocessingModel,
PagePreprocessingOptions,
)
from docling.models.picture_description_api_model import PictureDescriptionApiModel
from docling.models.picture_description_base_model import PictureDescriptionBaseModel
from docling.models.picture_description_vlm_model import PictureDescriptionVlmModel
from docling.models.rapid_ocr_model import RapidOcrModel
from docling.models.readingorder_model import ReadingOrderModel, ReadingOrderOptions
from docling.models.table_structure_model import TableStructureModel
from docling.models.tesseract_ocr_cli_model import TesseractOcrCliModel
from docling.models.tesseract_ocr_model import TesseractOcrModel
from docling.pipeline.base_pipeline import PaginatedPipeline
from docling.utils.model_downloader import download_models
from docling.utils.profiling import ProfilingScope, TimeRecorder
_log = logging.getLogger(__name__)
class StandardPdfPipeline(PaginatedPipeline):
_layout_model_path = "model_artifacts/layout"
_table_model_path = "model_artifacts/tableformer"
_layout_model_path = LayoutModel._model_path
_table_model_path = TableStructureModel._model_path
def __init__(self, pipeline_options: PdfPipelineOptions):
super().__init__(pipeline_options)
self.pipeline_options: PdfPipelineOptions
if pipeline_options.artifacts_path is None:
self.artifacts_path = self.download_models_hf()
else:
self.artifacts_path = Path(pipeline_options.artifacts_path)
artifacts_path: Optional[Path] = None
if pipeline_options.artifacts_path is not None:
artifacts_path = Path(pipeline_options.artifacts_path).expanduser()
elif settings.artifacts_path is not None:
artifacts_path = Path(settings.artifacts_path).expanduser()
if artifacts_path is not None and not artifacts_path.is_dir():
raise RuntimeError(
f"The value of {artifacts_path=} is not valid. "
"When defined, it must point to a folder containing all models required by the pipeline."
)
self.keep_images = (
self.pipeline_options.generate_page_images
@ -61,9 +76,9 @@ class StandardPdfPipeline(PaginatedPipeline):
or self.pipeline_options.generate_table_images
)
self.glm_model = GlmModel(options=GlmOptions())
self.glm_model = ReadingOrderModel(options=ReadingOrderOptions())
if (ocr_model := self.get_ocr_model()) is None:
if (ocr_model := self.get_ocr_model(artifacts_path=artifacts_path)) is None:
raise RuntimeError(
f"The specified OCR kind is not supported: {pipeline_options.ocr_options.kind}."
)
@ -79,15 +94,13 @@ class StandardPdfPipeline(PaginatedPipeline):
ocr_model,
# Layout model
LayoutModel(
artifacts_path=self.artifacts_path
/ StandardPdfPipeline._layout_model_path,
artifacts_path=artifacts_path,
accelerator_options=pipeline_options.accelerator_options,
),
# Table structure model
TableStructureModel(
enabled=pipeline_options.do_table_structure,
artifacts_path=self.artifacts_path
/ StandardPdfPipeline._table_model_path,
artifacts_path=artifacts_path,
options=pipeline_options.table_structure_options,
accelerator_options=pipeline_options.accelerator_options,
),
@ -95,13 +108,22 @@ class StandardPdfPipeline(PaginatedPipeline):
PageAssembleModel(options=PageAssembleOptions()),
]
# Picture description model
if (
picture_description_model := self.get_picture_description_model(
artifacts_path=artifacts_path
)
) is None:
raise RuntimeError(
f"The specified picture description kind is not supported: {pipeline_options.picture_description_options.kind}."
)
self.enrichment_pipe = [
# Other models working on `NodeItem` elements in the DoclingDocument
# Code Formula Enrichment Model
CodeFormulaModel(
enabled=pipeline_options.do_code_enrichment
or pipeline_options.do_formula_enrichment,
artifacts_path=pipeline_options.artifacts_path,
artifacts_path=artifacts_path,
options=CodeFormulaModelOptions(
do_code_enrichment=pipeline_options.do_code_enrichment,
do_formula_enrichment=pipeline_options.do_formula_enrichment,
@ -111,15 +133,18 @@ class StandardPdfPipeline(PaginatedPipeline):
# Document Picture Classifier
DocumentPictureClassifier(
enabled=pipeline_options.do_picture_classification,
artifacts_path=pipeline_options.artifacts_path,
artifacts_path=artifacts_path,
options=DocumentPictureClassifierOptions(),
accelerator_options=pipeline_options.accelerator_options,
),
# Document Picture description
picture_description_model,
]
if (
self.pipeline_options.do_formula_enrichment
or self.pipeline_options.do_code_enrichment
or self.pipeline_options.do_picture_description
):
self.keep_backend = True
@ -127,23 +152,24 @@ class StandardPdfPipeline(PaginatedPipeline):
def download_models_hf(
local_dir: Optional[Path] = None, force: bool = False
) -> Path:
from huggingface_hub import snapshot_download
from huggingface_hub.utils import disable_progress_bars
disable_progress_bars()
download_path = snapshot_download(
repo_id="ds4sd/docling-models",
force_download=force,
local_dir=local_dir,
revision="v2.1.0",
warnings.warn(
"The usage of StandardPdfPipeline.download_models_hf() is deprecated "
"use instead the utility `docling-tools models download`, or "
"the upstream method docling.utils.models_downloader.download_all()",
DeprecationWarning,
stacklevel=3,
)
return Path(download_path)
output_dir = download_models(output_dir=local_dir, force=force, progress=False)
return output_dir
def get_ocr_model(self) -> Optional[BaseOcrModel]:
def get_ocr_model(
self, artifacts_path: Optional[Path] = None
) -> Optional[BaseOcrModel]:
if isinstance(self.pipeline_options.ocr_options, EasyOcrOptions):
return EasyOcrModel(
enabled=self.pipeline_options.do_ocr,
artifacts_path=artifacts_path,
options=self.pipeline_options.ocr_options,
accelerator_options=self.pipeline_options.accelerator_options,
)
@ -174,6 +200,30 @@ class StandardPdfPipeline(PaginatedPipeline):
)
return None
def get_picture_description_model(
self, artifacts_path: Optional[Path] = None
) -> Optional[PictureDescriptionBaseModel]:
if isinstance(
self.pipeline_options.picture_description_options,
PictureDescriptionApiOptions,
):
return PictureDescriptionApiModel(
enabled=self.pipeline_options.do_picture_description,
enable_remote_services=self.pipeline_options.enable_remote_services,
options=self.pipeline_options.picture_description_options,
)
elif isinstance(
self.pipeline_options.picture_description_options,
PictureDescriptionVlmOptions,
):
return PictureDescriptionVlmModel(
enabled=self.pipeline_options.do_picture_description,
artifacts_path=artifacts_path,
options=self.pipeline_options.picture_description_options,
accelerator_options=self.pipeline_options.accelerator_options,
)
return None
def initialize_page(self, conv_res: ConversionResult, page: Page) -> Page:
with TimeRecorder(conv_res, "page_init"):
page._backend = conv_res.input._backend.load_page(page.page_no) # type: ignore

534
docling/pipeline/vlm_pipeline.py Normal file
View File

@ -0,0 +1,534 @@
import itertools
import logging
import re
import warnings
from io import BytesIO
# from io import BytesIO
from pathlib import Path
from typing import Optional
from docling_core.types import DoclingDocument
from docling_core.types.doc import (
BoundingBox,
DocItem,
DocItemLabel,
DoclingDocument,
GroupLabel,
ImageRef,
ImageRefMode,
PictureItem,
ProvenanceItem,
Size,
TableCell,
TableData,
TableItem,
)
from docling_core.types.doc.tokens import DocumentToken, TableToken
from docling.backend.abstract_backend import AbstractDocumentBackend
from docling.backend.md_backend import MarkdownDocumentBackend
from docling.backend.pdf_backend import PdfDocumentBackend
from docling.datamodel.base_models import InputFormat, Page
from docling.datamodel.document import ConversionResult, InputDocument
from docling.datamodel.pipeline_options import (
PdfPipelineOptions,
ResponseFormat,
VlmPipelineOptions,
)
from docling.datamodel.settings import settings
from docling.models.hf_vlm_model import HuggingFaceVlmModel
from docling.pipeline.base_pipeline import PaginatedPipeline
from docling.utils.profiling import ProfilingScope, TimeRecorder
_log = logging.getLogger(__name__)
class VlmPipeline(PaginatedPipeline):
def __init__(self, pipeline_options: VlmPipelineOptions):
super().__init__(pipeline_options)
self.keep_backend = True
warnings.warn(
"The VlmPipeline is currently experimental and may change in upcoming versions without notice.",
category=UserWarning,
stacklevel=2,
)
self.pipeline_options: VlmPipelineOptions
artifacts_path: Optional[Path] = None
if pipeline_options.artifacts_path is not None:
artifacts_path = Path(pipeline_options.artifacts_path).expanduser()
elif settings.artifacts_path is not None:
artifacts_path = Path(settings.artifacts_path).expanduser()
if artifacts_path is not None and not artifacts_path.is_dir():
raise RuntimeError(
f"The value of {artifacts_path=} is not valid. "
"When defined, it must point to a folder containing all models required by the pipeline."
)
# force_backend_text = False - use text that is coming from VLM response
# force_backend_text = True - get text from backend using bounding boxes predicted by SmolDocling doctags
self.force_backend_text = (
pipeline_options.force_backend_text
and pipeline_options.vlm_options.response_format == ResponseFormat.DOCTAGS
)
self.keep_images = self.pipeline_options.generate_page_images
self.build_pipe = [
HuggingFaceVlmModel(
enabled=True, # must be always enabled for this pipeline to make sense.
artifacts_path=artifacts_path,
accelerator_options=pipeline_options.accelerator_options,
vlm_options=self.pipeline_options.vlm_options,
),
]
self.enrichment_pipe = [
# Other models working on `NodeItem` elements in the DoclingDocument
]
def initialize_page(self, conv_res: ConversionResult, page: Page) -> Page:
with TimeRecorder(conv_res, "page_init"):
page._backend = conv_res.input._backend.load_page(page.page_no) # type: ignore
if page._backend is not None and page._backend.is_valid():
page.size = page._backend.get_size()
return page
def _assemble_document(self, conv_res: ConversionResult) -> ConversionResult:
with TimeRecorder(conv_res, "doc_assemble", scope=ProfilingScope.DOCUMENT):
if (
self.pipeline_options.vlm_options.response_format
== ResponseFormat.DOCTAGS
):
conv_res.document = self._turn_tags_into_doc(conv_res.pages)
elif (
self.pipeline_options.vlm_options.response_format
== ResponseFormat.MARKDOWN
):
conv_res.document = self._turn_md_into_doc(conv_res)
else:
raise RuntimeError(
f"Unsupported VLM response format {self.pipeline_options.vlm_options.response_format}"
)
# Generate images of the requested element types
if self.pipeline_options.generate_picture_images:
scale = self.pipeline_options.images_scale
for element, _level in conv_res.document.iterate_items():
if not isinstance(element, DocItem) or len(element.prov) == 0:
continue
if (
isinstance(element, PictureItem)
and self.pipeline_options.generate_picture_images
):
page_ix = element.prov[0].page_no - 1
page = conv_res.pages[page_ix]
assert page.size is not None
assert page.image is not None
crop_bbox = (
element.prov[0]
.bbox.scaled(scale=scale)
.to_top_left_origin(page_height=page.size.height * scale)
)
cropped_im = page.image.crop(crop_bbox.as_tuple())
element.image = ImageRef.from_pil(
cropped_im, dpi=int(72 * scale)
)
return conv_res
def _turn_md_into_doc(self, conv_res):
predicted_text = ""
for pg_idx, page in enumerate(conv_res.pages):
if page.predictions.vlm_response:
predicted_text += page.predictions.vlm_response.text + "\n\n"
response_bytes = BytesIO(predicted_text.encode("utf8"))
out_doc = InputDocument(
path_or_stream=response_bytes,
filename=conv_res.input.file.name,
format=InputFormat.MD,
backend=MarkdownDocumentBackend,
)
backend = MarkdownDocumentBackend(
in_doc=out_doc,
path_or_stream=response_bytes,
)
return backend.convert()
def _turn_tags_into_doc(self, pages: list[Page]) -> DoclingDocument:
###############################################
# Tag definitions and color mappings
###############################################
# Maps the recognized tag to a Docling label.
# Code items will be given DocItemLabel.CODE
tag_to_doclabel = {
"title": DocItemLabel.TITLE,
"document_index": DocItemLabel.DOCUMENT_INDEX,
"otsl": DocItemLabel.TABLE,
"section_header_level_1": DocItemLabel.SECTION_HEADER,
"checkbox_selected": DocItemLabel.CHECKBOX_SELECTED,
"checkbox_unselected": DocItemLabel.CHECKBOX_UNSELECTED,
"text": DocItemLabel.TEXT,
"page_header": DocItemLabel.PAGE_HEADER,
"page_footer": DocItemLabel.PAGE_FOOTER,
"formula": DocItemLabel.FORMULA,
"caption": DocItemLabel.CAPTION,
"picture": DocItemLabel.PICTURE,
"list_item": DocItemLabel.LIST_ITEM,
"footnote": DocItemLabel.FOOTNOTE,
"code": DocItemLabel.CODE,
}
# Maps each tag to an associated bounding box color.
tag_to_color = {
"title": "blue",
"document_index": "darkblue",
"otsl": "green",
"section_header_level_1": "purple",
"checkbox_selected": "black",
"checkbox_unselected": "gray",
"text": "red",
"page_header": "orange",
"page_footer": "cyan",
"formula": "pink",
"caption": "magenta",
"picture": "yellow",
"list_item": "brown",
"footnote": "darkred",
"code": "lightblue",
}
def extract_bounding_box(text_chunk: str) -> Optional[BoundingBox]:
"""Extracts <loc_...> bounding box coords from the chunk, normalized by / 500."""
coords = re.findall(r"<loc_(\d+)>", text_chunk)
if len(coords) == 4:
l, t, r, b = map(float, coords)
return BoundingBox(l=l / 500, t=t / 500, r=r / 500, b=b / 500)
return None
def extract_inner_text(text_chunk: str) -> str:
"""Strips all <...> tags inside the chunk to get the raw text content."""
return re.sub(r"<.*?>", "", text_chunk, flags=re.DOTALL).strip()
def extract_text_from_backend(page: Page, bbox: BoundingBox | None) -> str:
# Convert bounding box normalized to 0-100 into page coordinates for cropping
text = ""
if bbox:
if page.size:
bbox.l = bbox.l * page.size.width
bbox.t = bbox.t * page.size.height
bbox.r = bbox.r * page.size.width
bbox.b = bbox.b * page.size.height
if page._backend:
text = page._backend.get_text_in_rect(bbox)
return text
def otsl_parse_texts(texts, tokens):
split_word = TableToken.OTSL_NL.value
split_row_tokens = [
list(y)
for x, y in itertools.groupby(tokens, lambda z: z == split_word)
if not x
]
table_cells = []
r_idx = 0
c_idx = 0
def count_right(tokens, c_idx, r_idx, which_tokens):
span = 0
c_idx_iter = c_idx
while tokens[r_idx][c_idx_iter] in which_tokens:
c_idx_iter += 1
span += 1
if c_idx_iter >= len(tokens[r_idx]):
return span
return span
def count_down(tokens, c_idx, r_idx, which_tokens):
span = 0
r_idx_iter = r_idx
while tokens[r_idx_iter][c_idx] in which_tokens:
r_idx_iter += 1
span += 1
if r_idx_iter >= len(tokens):
return span
return span
for i, text in enumerate(texts):
cell_text = ""
if text in [
TableToken.OTSL_FCEL.value,
TableToken.OTSL_ECEL.value,
TableToken.OTSL_CHED.value,
TableToken.OTSL_RHED.value,
TableToken.OTSL_SROW.value,
]:
row_span = 1
col_span = 1
right_offset = 1
if text != TableToken.OTSL_ECEL.value:
cell_text = texts[i + 1]
right_offset = 2
# Check next element(s) for lcel / ucel / xcel, set properly row_span, col_span
next_right_cell = ""
if i + right_offset < len(texts):
next_right_cell = texts[i + right_offset]
next_bottom_cell = ""
if r_idx + 1 < len(split_row_tokens):
if c_idx < len(split_row_tokens[r_idx + 1]):
next_bottom_cell = split_row_tokens[r_idx + 1][c_idx]
if next_right_cell in [
TableToken.OTSL_LCEL.value,
TableToken.OTSL_XCEL.value,
]:
# we have horisontal spanning cell or 2d spanning cell
col_span += count_right(
split_row_tokens,
c_idx + 1,
r_idx,
[TableToken.OTSL_LCEL.value, TableToken.OTSL_XCEL.value],
)
if next_bottom_cell in [
TableToken.OTSL_UCEL.value,
TableToken.OTSL_XCEL.value,
]:
# we have a vertical spanning cell or 2d spanning cell
row_span += count_down(
split_row_tokens,
c_idx,
r_idx + 1,
[TableToken.OTSL_UCEL.value, TableToken.OTSL_XCEL.value],
)
table_cells.append(
TableCell(
text=cell_text.strip(),
row_span=row_span,
col_span=col_span,
start_row_offset_idx=r_idx,
end_row_offset_idx=r_idx + row_span,
start_col_offset_idx=c_idx,
end_col_offset_idx=c_idx + col_span,
)
)
if text in [
TableToken.OTSL_FCEL.value,
TableToken.OTSL_ECEL.value,
TableToken.OTSL_CHED.value,
TableToken.OTSL_RHED.value,
TableToken.OTSL_SROW.value,
TableToken.OTSL_LCEL.value,
TableToken.OTSL_UCEL.value,
TableToken.OTSL_XCEL.value,
]:
c_idx += 1
if text == TableToken.OTSL_NL.value:
r_idx += 1
c_idx = 0
return table_cells, split_row_tokens
def otsl_extract_tokens_and_text(s: str):
# Pattern to match anything enclosed by < > (including the angle brackets themselves)
pattern = r"(<[^>]+>)"
# Find all tokens (e.g. "<otsl>", "<loc_140>", etc.)
tokens = re.findall(pattern, s)
# Remove any tokens that start with "<loc_"
tokens = [
token
for token in tokens
if not (
token.startswith(rf"<{DocumentToken.LOC.value}")
or token
in [
rf"<{DocumentToken.OTSL.value}>",
rf"</{DocumentToken.OTSL.value}>",
]
)
]
# Split the string by those tokens to get the in-between text
text_parts = re.split(pattern, s)
text_parts = [
token
for token in text_parts
if not (
token.startswith(rf"<{DocumentToken.LOC.value}")
or token
in [
rf"<{DocumentToken.OTSL.value}>",
rf"</{DocumentToken.OTSL.value}>",
]
)
]
# Remove any empty or purely whitespace strings from text_parts
text_parts = [part for part in text_parts if part.strip()]
return tokens, text_parts
def parse_table_content(otsl_content: str) -> TableData:
tokens, mixed_texts = otsl_extract_tokens_and_text(otsl_content)
table_cells, split_row_tokens = otsl_parse_texts(mixed_texts, tokens)
return TableData(
num_rows=len(split_row_tokens),
num_cols=(
max(len(row) for row in split_row_tokens) if split_row_tokens else 0
),
table_cells=table_cells,
)
doc = DoclingDocument(name="Document")
for pg_idx, page in enumerate(pages):
xml_content = ""
predicted_text = ""
if page.predictions.vlm_response:
predicted_text = page.predictions.vlm_response.text
image = page.image
page_no = pg_idx + 1
bounding_boxes = []
if page.size:
pg_width = page.size.width
pg_height = page.size.height
size = Size(width=pg_width, height=pg_height)
parent_page = doc.add_page(page_no=page_no, size=size)
"""
1. Finds all <tag>...</tag> blocks in the entire string (multi-line friendly) in the order they appear.
2. For each chunk, extracts bounding box (if any) and inner text.
3. Adds the item to a DoclingDocument structure with the right label.
4. Tracks bounding boxes + color in a separate list for later visualization.
"""
# Regex for all recognized tags
tag_pattern = (
rf"<(?P<tag>{DocItemLabel.TITLE}|{DocItemLabel.DOCUMENT_INDEX}|"
rf"{DocItemLabel.CHECKBOX_UNSELECTED}|{DocItemLabel.CHECKBOX_SELECTED}|"
rf"{DocItemLabel.TEXT}|{DocItemLabel.PAGE_HEADER}|"
rf"{DocItemLabel.PAGE_FOOTER}|{DocItemLabel.FORMULA}|"
rf"{DocItemLabel.CAPTION}|{DocItemLabel.PICTURE}|"
rf"{DocItemLabel.LIST_ITEM}|{DocItemLabel.FOOTNOTE}|{DocItemLabel.CODE}|"
rf"{DocItemLabel.SECTION_HEADER}_level_1|{DocumentToken.OTSL.value})>.*?</(?P=tag)>"
)
# DocumentToken.OTSL
pattern = re.compile(tag_pattern, re.DOTALL)
# Go through each match in order
for match in pattern.finditer(predicted_text):
full_chunk = match.group(0)
tag_name = match.group("tag")
bbox = extract_bounding_box(full_chunk)
doc_label = tag_to_doclabel.get(tag_name, DocItemLabel.PARAGRAPH)
color = tag_to_color.get(tag_name, "white")
# Store bounding box + color
if bbox:
bounding_boxes.append((bbox, color))
if tag_name == DocumentToken.OTSL.value:
table_data = parse_table_content(full_chunk)
bbox = extract_bounding_box(full_chunk)
if bbox:
prov = ProvenanceItem(
bbox=bbox.resize_by_scale(pg_width, pg_height),
charspan=(0, 0),
page_no=page_no,
)
doc.add_table(data=table_data, prov=prov)
else:
doc.add_table(data=table_data)
elif tag_name == DocItemLabel.PICTURE:
text_caption_content = extract_inner_text(full_chunk)
if image:
if bbox:
im_width, im_height = image.size
crop_box = (
int(bbox.l * im_width),
int(bbox.t * im_height),
int(bbox.r * im_width),
int(bbox.b * im_height),
)
cropped_image = image.crop(crop_box)
pic = doc.add_picture(
parent=None,
image=ImageRef.from_pil(image=cropped_image, dpi=72),
prov=(
ProvenanceItem(
bbox=bbox.resize_by_scale(pg_width, pg_height),
charspan=(0, 0),
page_no=page_no,
)
),
)
# If there is a caption to an image, add it as well
if len(text_caption_content) > 0:
caption_item = doc.add_text(
label=DocItemLabel.CAPTION,
text=text_caption_content,
parent=None,
)
pic.captions.append(caption_item.get_ref())
else:
if bbox:
# In case we don't have access to an binary of an image
doc.add_picture(
parent=None,
prov=ProvenanceItem(
bbox=bbox, charspan=(0, 0), page_no=page_no
),
)
# If there is a caption to an image, add it as well
if len(text_caption_content) > 0:
caption_item = doc.add_text(
label=DocItemLabel.CAPTION,
text=text_caption_content,
parent=None,
)
pic.captions.append(caption_item.get_ref())
else:
# For everything else, treat as text
if self.force_backend_text:
text_content = extract_text_from_backend(page, bbox)
else:
text_content = extract_inner_text(full_chunk)
doc.add_text(
label=doc_label,
text=text_content,
prov=(
ProvenanceItem(
bbox=bbox.resize_by_scale(pg_width, pg_height),
charspan=(0, len(text_content)),
page_no=page_no,
)
if bbox
else None
),
)
return doc
@classmethod
def get_default_options(cls) -> VlmPipelineOptions:
return VlmPipelineOptions()
@classmethod
def is_backend_supported(cls, backend: AbstractDocumentBackend):
return isinstance(backend, PdfDocumentBackend)

56
docling/utils/accelerator_utils.py
View File

@ -7,36 +7,62 @@ from docling.datamodel.pipeline_options import AcceleratorDevice
_log = logging.getLogger(__name__)
def decide_device(accelerator_device: AcceleratorDevice) -> str:
def decide_device(accelerator_device: str) -> str:
r"""
Resolve the device based on the acceleration options and the available devices in the system
Resolve the device based on the acceleration options and the available devices in the system.
Rules:
1. AUTO: Check for the best available device on the system.
2. User-defined: Check if the device actually exists, otherwise fall-back to CPU
"""
cuda_index = 0
device = "cpu"
has_cuda = torch.backends.cuda.is_built() and torch.cuda.is_available()
has_mps = torch.backends.mps.is_built() and torch.backends.mps.is_available()
if accelerator_device == AcceleratorDevice.AUTO:
if accelerator_device == AcceleratorDevice.AUTO.value: # Handle 'auto'
if has_cuda:
device = f"cuda:{cuda_index}"
device = "cuda:0"
elif has_mps:
device = "mps"
elif accelerator_device.startswith("cuda"):
if has_cuda:
# if cuda device index specified extract device id
parts = accelerator_device.split(":")
if len(parts) == 2 and parts[1].isdigit():
# select cuda device's id
cuda_index = int(parts[1])
if cuda_index < torch.cuda.device_count():
device = f"cuda:{cuda_index}"
else:
_log.warning(
"CUDA device 'cuda:%d' is not available. Fall back to 'CPU'.",
cuda_index,
)
elif len(parts) == 1: # just "cuda"
device = "cuda:0"
else:
_log.warning(
"Invalid CUDA device format '%s'. Fall back to 'CPU'",
accelerator_device,
)
else:
_log.warning("CUDA is not available in the system. Fall back to 'CPU'")
elif accelerator_device == AcceleratorDevice.MPS.value:
if has_mps:
device = "mps"
else:
_log.warning("MPS is not available in the system. Fall back to 'CPU'")
elif accelerator_device == AcceleratorDevice.CPU.value:
device = "cpu"
else:
if accelerator_device == AcceleratorDevice.CUDA:
if has_cuda:
device = f"cuda:{cuda_index}"
else:
_log.warning("CUDA is not available in the system. Fall back to 'CPU'")
elif accelerator_device == AcceleratorDevice.MPS:
if has_mps:
device = "mps"
else:
_log.warning("MPS is not available in the system. Fall back to 'CPU'")
_log.warning(
"Unknown device option '%s'. Fall back to 'CPU'", accelerator_device
)
_log.info("Accelerator device: '%s'", device)
return device

14
docling/utils/glm_utils.py
View File

@ -15,6 +15,7 @@ from docling_core.types.doc import (
TableCell,
TableData,
)
from docling_core.types.doc.document import ContentLayer
def resolve_item(paths, obj):
@ -307,6 +308,19 @@ def to_docling_document(doc_glm, update_name_label=False) -> DoclingDocument:
current_list = None
doc.add_code(text=text, prov=prov)
elif label == DocItemLabel.FORMULA:
current_list = None
doc.add_text(label=DocItemLabel.FORMULA, text="", orig=text, prov=prov)
elif label in [DocItemLabel.PAGE_HEADER, DocItemLabel.PAGE_FOOTER]:
current_list = None
doc.add_text(
label=DocItemLabel(name_label),
text=text,
prov=prov,
content_layer=ContentLayer.FURNITURE,
)
else:
current_list = None

3
docling/utils/locks.py Normal file
View File

@ -0,0 +1,3 @@
import threading
pypdfium2_lock = threading.Lock()

97
docling/utils/model_downloader.py Normal file
View File

@ -0,0 +1,97 @@
import logging
from pathlib import Path
from typing import Optional
from docling.datamodel.pipeline_options import (
granite_picture_description,
smolvlm_picture_description,
)
from docling.datamodel.settings import settings
from docling.models.code_formula_model import CodeFormulaModel
from docling.models.document_picture_classifier import DocumentPictureClassifier
from docling.models.easyocr_model import EasyOcrModel
from docling.models.layout_model import LayoutModel
from docling.models.picture_description_vlm_model import PictureDescriptionVlmModel
from docling.models.table_structure_model import TableStructureModel
_log = logging.getLogger(__name__)
def download_models(
output_dir: Optional[Path] = None,
*,
force: bool = False,
progress: bool = False,
with_layout: bool = True,
with_tableformer: bool = True,
with_code_formula: bool = True,
with_picture_classifier: bool = True,
with_smolvlm: bool = False,
with_granite_vision: bool = False,
with_easyocr: bool = True,
):
if output_dir is None:
output_dir = settings.cache_dir / "models"
# Make sure the folder exists
output_dir.mkdir(exist_ok=True, parents=True)
if with_layout:
_log.info(f"Downloading layout model...")
LayoutModel.download_models(
local_dir=output_dir / LayoutModel._model_repo_folder,
force=force,
progress=progress,
)
if with_tableformer:
_log.info(f"Downloading tableformer model...")
TableStructureModel.download_models(
local_dir=output_dir / TableStructureModel._model_repo_folder,
force=force,
progress=progress,
)
if with_picture_classifier:
_log.info(f"Downloading picture classifier model...")
DocumentPictureClassifier.download_models(
local_dir=output_dir / DocumentPictureClassifier._model_repo_folder,
force=force,
progress=progress,
)
if with_code_formula:
_log.info(f"Downloading code formula model...")
CodeFormulaModel.download_models(
local_dir=output_dir / CodeFormulaModel._model_repo_folder,
force=force,
progress=progress,
)
if with_smolvlm:
_log.info(f"Downloading SmolVlm model...")
PictureDescriptionVlmModel.download_models(
repo_id=smolvlm_picture_description.repo_id,
local_dir=output_dir / smolvlm_picture_description.repo_cache_folder,
force=force,
progress=progress,
)
if with_granite_vision:
_log.info(f"Downloading Granite Vision model...")
PictureDescriptionVlmModel.download_models(
repo_id=granite_picture_description.repo_id,
local_dir=output_dir / granite_picture_description.repo_cache_folder,
force=force,
progress=progress,
)
if with_easyocr:
_log.info(f"Downloading easyocr models...")
EasyOcrModel.download_models(
local_dir=output_dir / EasyOcrModel._model_repo_folder,
force=force,
progress=progress,
)
return output_dir

24
docling/utils/utils.py
View File

@ -4,6 +4,9 @@ from itertools import islice
from pathlib import Path
from typing import List, Union
import requests
from tqdm import tqdm
def chunkify(iterator, chunk_size):
"""Yield successive chunks of chunk_size from the iterable."""
@ -39,3 +42,24 @@ def create_hash(string: str):
hasher.update(string.encode("utf-8"))
return hasher.hexdigest()
def download_url_with_progress(url: str, progress: bool = False) -> BytesIO:
buf = BytesIO()
with requests.get(url, stream=True, allow_redirects=True) as response:
total_size = int(response.headers.get("content-length", 0))
progress_bar = tqdm(
total=total_size,
unit="B",
unit_scale=True,
unit_divisor=1024,
disable=(not progress),
)
for chunk in response.iter_content(10 * 1024):
buf.write(chunk)
progress_bar.update(len(chunk))
progress_bar.close()
buf.seek(0)
return buf

5
docling/utils/visualization.py
View File

@ -43,6 +43,11 @@ def draw_clusters(
y0 *= scale_x
y1 *= scale_y
if y1 <= y0:
y1, y0 = y0, y1
if x1 <= x0:
x1, x0 = x0, x1
cluster_fill_color = (*list(DocItemLabel.get_color(c.label)), 70)
cluster_outline_color = (
*list(DocItemLabel.get_color(c.label)),

13
docs/concepts/chunking.md
View File

@ -1,5 +1,18 @@
## Introduction
!!! note "Chunking approaches"
Starting from a `DoclingDocument`, there are in principle two possible chunking
approaches:
1. exporting the `DoclingDocument` to Markdown (or similar format) and then
performing user-defined chunking as a post-processing step, or
2. using native Docling chunkers, i.e. operating directly on the `DoclingDocument`
This page is about the latter, i.e. using native Docling chunkers.
For an example of using approach (1) check out e.g.
[this recipe](../examples/rag_langchain.ipynb) looking at the Markdown export mode.
A *chunker* is a Docling abstraction that, given a
[`DoclingDocument`](./docling_document.md), returns a stream of chunks, each of which
captures some part of the document as a string accompanied by respective metadata.

80
docs/examples/backend_csv.ipynb Normal file
View File

@ -0,0 +1,80 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conversion of CSV files\n",
"\n",
"This example shows how to convert CSV files to a structured Docling Document.\n",
"\n",
"* Multiple delimiters are supported: `,` `;` `|` `[tab]`\n",
"* Additional CSV dialect settings are detected automatically (e.g. quotes, line separator, escape character)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example Code"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"\n",
"from docling.document_converter import DocumentConverter\n",
"\n",
"# Convert CSV to Docling document\n",
"converter = DocumentConverter()\n",
"result = converter.convert(Path(\"../../tests/data/csv/csv-comma.csv\"))\n",
"output = result.document.export_to_markdown()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This code generates the following output:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website |\n",
"|---------|-----------------|--------------|-------------|---------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------|\n",
"| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ |\n",
"| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano, Dr | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ |\n",
"| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ |\n",
"| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez, Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ |\n",
"| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin, Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ |"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "docling-TtEIaPrw-py3.12",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

194
docs/examples/backend_xml_rag.ipynb
View File

@ -82,7 +82,7 @@
"from docling.document_converter import DocumentConverter\n",
"\n",
"# a sample PMC article:\n",
"source = \"../../tests/data/pubmed/elife-56337.nxml\"\n",
"source = \"../../tests/data/jats/elife-56337.nxml\"\n",
"converter = DocumentConverter()\n",
"result = converter.convert(source)\n",
"print(result.status)"
@ -97,7 +97,7 @@
},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": 2,
"metadata": {},
"outputs": [
{
@ -106,11 +106,11 @@
"text": [
"# KRAB-zinc finger protein gene expansion in response to active retrotransposons in the murine lineage\n",
"\n",
"Wolf Gernot; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; de Iaco Alberto; 2: School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL): Lausanne: Switzerland; Sun Ming-An; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Bruno Melania; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Tinkham Matthew; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Hoang Don; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Mitra Apratim; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Ralls Sherry; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States; Trono Didier; 2: School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL): Lausanne: Switzerland; Macfarlan Todd S; 1: The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health: Bethesda: United States\n",
"Gernot Wolf, Alberto de Iaco, Ming-An Sun, Melania Bruno, Matthew Tinkham, Don Hoang, Apratim Mitra, Sherry Ralls, Didier Trono, Todd S Macfarlan\n",
"\n",
"The Eunice Kennedy Shriver National Institute of Child Health and Human Development, The National Institutes of Health, Bethesda, United States; School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland\n",
"\n",
"## Abstract\n",
"\n",
"The Krüppel-associated box zinc finger protein (KRAB-ZFP) family diversified in mammals. The majority of human KRAB-ZFPs bind transposable elements (TEs), however, since most TEs are inactive in humans it is unclear whether KRAB-ZFPs emerged to suppress TEs. We demonstrate that many recently emerged murine KRAB-ZFPs also bind to TEs, including the active ETn, IAP, and L1 families. Using a CRISPR/Cas9-based engineering approach, we genetically deleted five large clusters of KRAB-ZFPs and demonstrate that target TEs are de-repressed, unleashing TE-encoded enhancers. Homozygous knockout mice lacking one of two KRAB-ZFP gene clusters on chromosome 2 and chromosome 4 were nonetheless viable. In pedigrees of chromosome 4 cluster KRAB-ZFP mutants, we identified numerous novel ETn insertions with a modest increase in mutants. Our data strongly support the current model that recent waves of retrotransposon activity drove the expansion of KRAB-ZFP genes in mice and that many KRAB-ZFPs play a redundant role restricting TE activity.\n",
"\n"
]
}
@ -131,7 +131,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"metadata": {},
"outputs": [
{
@ -198,7 +198,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 4,
"metadata": {},
"outputs": [
{
@ -224,7 +224,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@ -261,7 +261,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@ -313,7 +313,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 7,
"metadata": {},
"outputs": [
{
@ -359,9 +359,18 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading https://bulkdata.uspto.gov/data/patent/grant/redbook/fulltext/2024/ipg241217.zip...\n",
"Parsing zip file, splitting into XML sections, and exporting to files...\n"
]
}
],
"source": [
"import zipfile\n",
"\n",
@ -407,7 +416,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 9,
"metadata": {},
"outputs": [
{
@ -435,7 +444,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 11,
"metadata": {},
"outputs": [
{
@ -449,7 +458,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3964d1ff30f74588a2f6b53ca8865a9f",
"model_id": "316241ca89a843bda3170f2a5c76c639",
"version_major": 2,
"version_minor": 0
},
@ -471,7 +480,7 @@
"source": [
"from tqdm.notebook import tqdm\n",
"\n",
"from docling.backend.xml.pubmed_backend import PubMedDocumentBackend\n",
"from docling.backend.xml.jats_backend import JatsDocumentBackend\n",
"from docling.backend.xml.uspto_backend import PatentUsptoDocumentBackend\n",
"from docling.datamodel.base_models import InputFormat\n",
"from docling.datamodel.document import InputDocument\n",
@ -479,10 +488,10 @@
"# check PMC\n",
"in_doc = InputDocument(\n",
" path_or_stream=TEMP_DIR / \"nihpp-2024.12.26.630351v1.nxml\",\n",
" format=InputFormat.XML_PUBMED,\n",
" backend=PubMedDocumentBackend,\n",
" format=InputFormat.XML_JATS,\n",
" backend=JatsDocumentBackend,\n",
")\n",
"backend = PubMedDocumentBackend(\n",
"backend = JatsDocumentBackend(\n",
" in_doc=in_doc, path_or_stream=TEMP_DIR / \"nihpp-2024.12.26.630351v1.nxml\"\n",
")\n",
"print(f\"Document {in_doc.file.name} is a valid PMC article? {backend.is_valid()}\")\n",
@ -521,7 +530,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 12,
"metadata": {},
"outputs": [
{
@ -543,7 +552,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"✏️ **Tip**: in general, there is no need to use the backend converters to parse USPTO or PubMed XML files. The generic `DocumentConverter` object tries to guess the input document format and applies the corresponding backend parser. The conversion shown in [Simple Conversion](#simple-conversion) is the recommended usage for the supported XML files."
"✏️ **Tip**: in general, there is no need to use the backend converters to parse USPTO or JATS (PubMed) XML files. The generic `DocumentConverter` object tries to guess the input document format and applies the corresponding backend parser. The conversion shown in [Simple Conversion](#simple-conversion) is the recommended usage for the supported XML files."
]
},
{
@ -579,7 +588,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@ -607,7 +616,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@ -625,144 +634,9 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-01-24 16:49:57,108 [DEBUG][_create_connection]: Created new connection using: 2d58fad6c63448a486c0c0ffe3b7b28c (async_milvus_client.py:600)\n",
"Loading files: 51%|█████ | 51/100 [00:00<00:00, 67.88file/s]Input document ipg241217-1050.xml does not match any allowed format.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Failed to load file /var/folders/2r/b2sdj1512g1_0m7wzzy7sftr0000gn/T/tmp11rjcdj8/ipg241217-1050.xml with error: File format not allowed: /var/folders/2r/b2sdj1512g1_0m7wzzy7sftr0000gn/T/tmp11rjcdj8/ipg241217-1050.xml. Skipping...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading files: 100%|██████████| 100/100 [00:01<00:00, 58.05file/s]\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e9208639f1a4418d97267a28305d18fa",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Parsing nodes: 0%| | 0/99 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "88026613f6f44f0c8476dceaa1cb78cd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Generating embeddings: 0%| | 0/2048 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7522b8b434b54616b4cfc3d71e9556d7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Generating embeddings: 0%| | 0/2048 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5879d8161c2041f5b100959e69ff9017",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Generating embeddings: 0%| | 0/2048 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "557912b5e3c741f3a06127156bc46379",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Generating embeddings: 0%| | 0/2048 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "843bb145942b449aa55fc5b8208da734",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Generating embeddings: 0%| | 0/2048 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c7dba09a4aed422998e9b9c2c3a70317",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Generating embeddings: 0%| | 0/2048 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0bd031356c7e4e879dcbe1d04e6c4a4e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Generating embeddings: 0%| | 0/425 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"outputs": [],
"source": [
"from llama_index.core import StorageContext, VectorStoreIndex\n",
"from llama_index.vector_stores.milvus import MilvusVectorStore\n",

62
docs/examples/batch_convert.py
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@ -5,16 +5,18 @@ from pathlib import Path
from typing import Iterable
import yaml
from docling_core.types.doc import ImageRefMode
from docling.datamodel.base_models import ConversionStatus
from docling.datamodel.base_models import ConversionStatus, InputFormat
from docling.datamodel.document import ConversionResult
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.datamodel.settings import settings
from docling.document_converter import DocumentConverter
from docling.document_converter import DocumentConverter, PdfFormatOption
_log = logging.getLogger(__name__)
USE_V2 = True
USE_LEGACY = True
USE_LEGACY = False
def export_documents(
@ -33,26 +35,31 @@ def export_documents(
doc_filename = conv_res.input.file.stem
if USE_V2:
# Export Docling document format to JSON:
with (output_dir / f"{doc_filename}.json").open("w") as fp:
fp.write(json.dumps(conv_res.document.export_to_dict()))
conv_res.document.save_as_json(
output_dir / f"{doc_filename}.json",
image_mode=ImageRefMode.PLACEHOLDER,
)
conv_res.document.save_as_html(
output_dir / f"{doc_filename}.html",
image_mode=ImageRefMode.EMBEDDED,
)
conv_res.document.save_as_document_tokens(
output_dir / f"{doc_filename}.doctags.txt"
)
conv_res.document.save_as_markdown(
output_dir / f"{doc_filename}.md",
image_mode=ImageRefMode.PLACEHOLDER,
)
conv_res.document.save_as_markdown(
output_dir / f"{doc_filename}.txt",
image_mode=ImageRefMode.PLACEHOLDER,
strict_text=True,
)
# Export Docling document format to YAML:
with (output_dir / f"{doc_filename}.yaml").open("w") as fp:
fp.write(yaml.safe_dump(conv_res.document.export_to_dict()))
# Export Docling document format to doctags:
with (output_dir / f"{doc_filename}.doctags.txt").open("w") as fp:
fp.write(conv_res.document.export_to_document_tokens())
# Export Docling document format to markdown:
with (output_dir / f"{doc_filename}.md").open("w") as fp:
fp.write(conv_res.document.export_to_markdown())
# Export Docling document format to text:
with (output_dir / f"{doc_filename}.txt").open("w") as fp:
fp.write(conv_res.document.export_to_markdown(strict_text=True))
if USE_LEGACY:
# Export Deep Search document JSON format:
with (output_dir / f"{doc_filename}.legacy.json").open(
@ -103,10 +110,10 @@ def main():
logging.basicConfig(level=logging.INFO)
input_doc_paths = [
Path("./tests/data/2206.01062.pdf"),
Path("./tests/data/2203.01017v2.pdf"),
Path("./tests/data/2305.03393v1.pdf"),
Path("./tests/data/redp5110_sampled.pdf"),
Path("./tests/data/pdf/2206.01062.pdf"),
Path("./tests/data/pdf/2203.01017v2.pdf"),
Path("./tests/data/pdf/2305.03393v1.pdf"),
Path("./tests/data/pdf/redp5110_sampled.pdf"),
]
# buf = BytesIO(Path("./test/data/2206.01062.pdf").open("rb").read())
@ -119,13 +126,20 @@ def main():
# settings.debug.visualize_tables = True
# settings.debug.visualize_cells = True
doc_converter = DocumentConverter()
pipeline_options = PdfPipelineOptions()
pipeline_options.generate_page_images = True
doc_converter = DocumentConverter(
format_options={
InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
}
)
start_time = time.time()
conv_results = doc_converter.convert_all(
input_doc_paths,
raises_on_error=False, # to let conversion run through all and examine results at the end
raises_on_error=True, # to let conversion run through all and examine results at the end
)
success_count, partial_success_count, failure_count = export_documents(
conv_results, output_dir=Path("scratch")

2
docs/examples/custom_convert.py
View File

@ -21,7 +21,7 @@ _log = logging.getLogger(__name__)
def main():
logging.basicConfig(level=logging.INFO)
input_doc_path = Path("./tests/data/2206.01062.pdf")
input_doc_path = Path("./tests/data/pdf/2206.01062.pdf")
###########################################################################

6
docs/examples/develop_formula_understanding.py
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@ -1,3 +1,7 @@
# WARNING
# This example demonstrates only how to develop a new enrichment model.
# It does not run the actual formula understanding model.
import logging
from pathlib import Path
from typing import Iterable
@ -68,7 +72,7 @@ class ExampleFormulaUnderstandingPipeline(StandardPdfPipeline):
def main():
logging.basicConfig(level=logging.INFO)
input_doc_path = Path("./tests/data/2203.01017v2.pdf")
input_doc_path = Path("./tests/data/pdf/2203.01017v2.pdf")
pipeline_options = ExampleFormulaUnderstandingPipelineOptions()
pipeline_options.do_formula_understanding = True

6
docs/examples/develop_picture_enrichment.py
View File

@ -1,3 +1,7 @@
# WARNING
# This example demonstrates only how to develop a new enrichment model.
# It does not run the actual picture classifier model.
import logging
from pathlib import Path
from typing import Any, Iterable
@ -71,7 +75,7 @@ class ExamplePictureClassifierPipeline(StandardPdfPipeline):
def main():
logging.basicConfig(level=logging.INFO)
input_doc_path = Path("./tests/data/2206.01062.pdf")
input_doc_path = Path("./tests/data/pdf/2206.01062.pdf")
pipeline_options = ExamplePictureClassifierPipelineOptions()
pipeline_options.images_scale = 2.0

2
docs/examples/export_figures.py
View File

@ -16,7 +16,7 @@ IMAGE_RESOLUTION_SCALE = 2.0
def main():
logging.basicConfig(level=logging.INFO)
input_doc_path = Path("./tests/data/2206.01062.pdf")
input_doc_path = Path("./tests/data/pdf/2206.01062.pdf")
output_dir = Path("scratch")
# Important: For operating with page images, we must keep them, otherwise the DocumentConverter

2
docs/examples/export_multimodal.py
View File

@ -19,7 +19,7 @@ IMAGE_RESOLUTION_SCALE = 2.0
def main():
logging.basicConfig(level=logging.INFO)
input_doc_path = Path("./tests/data/2206.01062.pdf")
input_doc_path = Path("./tests/data/pdf/2206.01062.pdf")
output_dir = Path("scratch")
# Important: For operating with page images, we must keep them, otherwise the DocumentConverter

2
docs/examples/export_tables.py
View File

@ -12,7 +12,7 @@ _log = logging.getLogger(__name__)
def main():
logging.basicConfig(level=logging.INFO)
input_doc_path = Path("./tests/data/2206.01062.pdf")
input_doc_path = Path("./tests/data/pdf/2206.01062.pdf")
output_dir = Path("scratch")
doc_converter = DocumentConverter()

2
docs/examples/full_page_ocr.py
View File

@ -14,7 +14,7 @@ from docling.document_converter import DocumentConverter, PdfFormatOption
def main():
input_doc = Path("./tests/data/2206.01062.pdf")
input_doc = Path("./tests/data/pdf/2206.01062.pdf")
pipeline_options = PdfPipelineOptions()
pipeline_options.do_ocr = True

21
docs/examples/hybrid_chunking.ipynb
View File

@ -83,7 +83,15 @@
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Token indices sequence length is longer than the specified maximum sequence length for this model (531 > 512). Running this sequence through the model will result in indexing errors\n"
]
}
],
"source": [
"from docling.chunking import HybridChunker\n",
"\n",
@ -91,6 +99,13 @@
"chunk_iter = chunker.chunk(dl_doc=doc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 👉 **NOTE**: As you see above, using the `HybridChunker` can sometimes lead to a warning from the transformers library, however this is a \"false alarm\" — for details check [here](https://ds4sd.github.io/docling/faq/#hybridchunker-triggers-warning-token-indices-sequence-length-is-longer-than-the-specified-maximum-sequence-length-for-this-model)."
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -337,11 +352,11 @@
"source": [
"for i, chunk in enumerate(chunks):\n",
" print(f\"=== {i} ===\")\n",
" txt_tokens = len(tokenizer.tokenize(chunk.text, max_length=None))\n",
" txt_tokens = len(tokenizer.tokenize(chunk.text))\n",
" print(f\"chunk.text ({txt_tokens} tokens):\\n{repr(chunk.text)}\")\n",
"\n",
" ser_txt = chunker.serialize(chunk=chunk)\n",
" ser_tokens = len(tokenizer.tokenize(ser_txt, max_length=None))\n",
" ser_tokens = len(tokenizer.tokenize(ser_txt))\n",
" print(f\"chunker.serialize(chunk) ({ser_tokens} tokens):\\n{repr(ser_txt)}\")\n",
"\n",
" print()"

2
docs/examples/inspect_picture_content.py
View File

@ -4,7 +4,7 @@ from docling.datamodel.base_models import InputFormat
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.document_converter import DocumentConverter, PdfFormatOption
source = "tests/data/amt_handbook_sample.pdf"
source = "tests/data/pdf/amt_handbook_sample.pdf"
pipeline_options = PdfPipelineOptions()
pipeline_options.images_scale = 2

96
docs/examples/minimal_vlm_pipeline.py Normal file
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@ -0,0 +1,96 @@
import json
import time
from pathlib import Path
import yaml
from docling.datamodel.base_models import InputFormat
from docling.datamodel.pipeline_options import (
AcceleratorDevice,
VlmPipelineOptions,
granite_vision_vlm_conversion_options,
smoldocling_vlm_conversion_options,
)
from docling.datamodel.settings import settings
from docling.document_converter import DocumentConverter, PdfFormatOption
from docling.pipeline.vlm_pipeline import VlmPipeline
sources = [
"tests/data/2305.03393v1-pg9-img.png",
]
## Use experimental VlmPipeline
pipeline_options = VlmPipelineOptions()
# If force_backend_text = True, text from backend will be used instead of generated text
pipeline_options.force_backend_text = False
## On GPU systems, enable flash_attention_2 with CUDA:
# pipeline_options.accelerator_options.device = AcceleratorDevice.CUDA
# pipeline_options.accelerator_options.cuda_use_flash_attention2 = True
## Pick a VLM model. We choose SmolDocling-256M by default
pipeline_options.vlm_options = smoldocling_vlm_conversion_options
## Alternative VLM models:
# pipeline_options.vlm_options = granite_vision_vlm_conversion_options
from docling_core.types.doc import DocItemLabel, ImageRefMode
from docling_core.types.doc.document import DEFAULT_EXPORT_LABELS
## Set up pipeline for PDF or image inputs
converter = DocumentConverter(
format_options={
InputFormat.PDF: PdfFormatOption(
pipeline_cls=VlmPipeline,
pipeline_options=pipeline_options,
),
InputFormat.IMAGE: PdfFormatOption(
pipeline_cls=VlmPipeline,
pipeline_options=pipeline_options,
),
}
)
out_path = Path("scratch")
out_path.mkdir(parents=True, exist_ok=True)
for source in sources:
start_time = time.time()
print("================================================")
print("Processing... {}".format(source))
print("================================================")
print("")
res = converter.convert(source)
print("------------------------------------------------")
print("MD:")
print("------------------------------------------------")
print("")
print(res.document.export_to_markdown())
for page in res.pages:
print("")
print("Predicted page in DOCTAGS:")
print(page.predictions.vlm_response.text)
res.document.save_as_html(
filename=Path("{}/{}.html".format(out_path, res.input.file.stem)),
image_mode=ImageRefMode.REFERENCED,
labels=[*DEFAULT_EXPORT_LABELS, DocItemLabel.FOOTNOTE],
)
with (out_path / f"{res.input.file.stem}.json").open("w") as fp:
fp.write(json.dumps(res.document.export_to_dict()))
pg_num = res.document.num_pages()
print("")
inference_time = time.time() - start_time
print(
f"Total document prediction time: {inference_time:.2f} seconds, pages: {pg_num}"
)
print("================================================")
print("done!")
print("================================================")

343
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118
docs/examples/pictures_description_api.py Normal file
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@ -0,0 +1,118 @@
import logging
import os
from pathlib import Path
import requests
from docling_core.types.doc import PictureItem
from dotenv import load_dotenv
from docling.datamodel.base_models import InputFormat
from docling.datamodel.pipeline_options import (
PdfPipelineOptions,
PictureDescriptionApiOptions,
)
from docling.document_converter import DocumentConverter, PdfFormatOption
def vllm_local_options(model: str):
options = PictureDescriptionApiOptions(
url="http://localhost:8000/v1/chat/completions",
params=dict(
model=model,
seed=42,
max_completion_tokens=200,
),
prompt="Describe the image in three sentences. Be consise and accurate.",
timeout=90,
)
return options
def watsonx_vlm_options():
load_dotenv()
api_key = os.environ.get("WX_API_KEY")
project_id = os.environ.get("WX_PROJECT_ID")
def _get_iam_access_token(api_key: str) -> str:
res = requests.post(
url="https://iam.cloud.ibm.com/identity/token",
headers={
"Content-Type": "application/x-www-form-urlencoded",
},
data=f"grant_type=urn:ibm:params:oauth:grant-type:apikey&apikey={api_key}",
)
res.raise_for_status()
api_out = res.json()
print(f"{api_out=}")
return api_out["access_token"]
options = PictureDescriptionApiOptions(
url="https://us-south.ml.cloud.ibm.com/ml/v1/text/chat?version=2023-05-29",
params=dict(
model_id="meta-llama/llama-3-2-11b-vision-instruct",
project_id=project_id,
parameters=dict(
max_new_tokens=400,
),
),
headers={
"Authorization": "Bearer " + _get_iam_access_token(api_key=api_key),
},
prompt="Describe the image in three sentences. Be consise and accurate.",
timeout=60,
)
return options
def main():
logging.basicConfig(level=logging.INFO)
input_doc_path = Path("./tests/data/pdf/2206.01062.pdf")
pipeline_options = PdfPipelineOptions(
enable_remote_services=True # <-- this is required!
)
pipeline_options.do_picture_description = True
# The PictureDescriptionApiOptions() allows to interface with APIs supporting
# the multi-modal chat interface. Here follow a few example on how to configure those.
#
# One possibility is self-hosting model, e.g. via VLLM.
# $ vllm serve MODEL_NAME
# Then PictureDescriptionApiOptions can point to the localhost endpoint.
#
# Example for the Granite Vision model: (uncomment the following lines)
# pipeline_options.picture_description_options = vllm_local_options(
# model="ibm-granite/granite-vision-3.1-2b-preview"
# )
#
# Example for the SmolVLM model: (uncomment the following lines)
pipeline_options.picture_description_options = vllm_local_options(
model="HuggingFaceTB/SmolVLM-256M-Instruct"
)
#
# Another possibility is using online services, e.g. watsonx.ai.
# Using requires setting the env variables WX_API_KEY and WX_PROJECT_ID.
# Uncomment the following line for this option:
# pipeline_options.picture_description_options = watsonx_vlm_options()
doc_converter = DocumentConverter(
format_options={
InputFormat.PDF: PdfFormatOption(
pipeline_options=pipeline_options,
)
}
)
result = doc_converter.convert(input_doc_path)
for element, _level in result.document.iterate_items():
if isinstance(element, PictureItem):
print(
f"Picture {element.self_ref}\n"
f"Caption: {element.caption_text(doc=result.document)}\n"
f"Annotations: {element.annotations}"
)
if __name__ == "__main__":
main()

58
docs/examples/rapidocr_with_custom_models.py Normal file
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@ -0,0 +1,58 @@
import os
from huggingface_hub import snapshot_download
from docling.datamodel.pipeline_options import PdfPipelineOptions, RapidOcrOptions
from docling.document_converter import (
ConversionResult,
DocumentConverter,
InputFormat,
PdfFormatOption,
)
def main():
# Source document to convert
source = "https://arxiv.org/pdf/2408.09869v4"
# Download RappidOCR models from HuggingFace
print("Downloading RapidOCR models")
download_path = snapshot_download(repo_id="SWHL/RapidOCR")
# Setup RapidOcrOptions for english detection
det_model_path = os.path.join(
download_path, "PP-OCRv4", "en_PP-OCRv3_det_infer.onnx"
)
rec_model_path = os.path.join(
download_path, "PP-OCRv4", "ch_PP-OCRv4_rec_server_infer.onnx"
)
cls_model_path = os.path.join(
download_path, "PP-OCRv3", "ch_ppocr_mobile_v2.0_cls_train.onnx"
)
ocr_options = RapidOcrOptions(
det_model_path=det_model_path,
rec_model_path=rec_model_path,
cls_model_path=cls_model_path,
)
pipeline_options = PdfPipelineOptions(
ocr_options=ocr_options,
)
# Convert the document
converter = DocumentConverter(
format_options={
InputFormat.PDF: PdfFormatOption(
pipeline_options=pipeline_options,
),
},
)
conversion_result: ConversionResult = converter.convert(source=source)
doc = conversion_result.document
md = doc.export_to_markdown()
print(md)
if __name__ == "__main__":
main()

5
docs/examples/run_with_accelerator.py
View File

@ -14,7 +14,7 @@ from docling.document_converter import DocumentConverter, PdfFormatOption
def main():
input_doc = Path("./tests/data/2206.01062.pdf")
input_doc = Path("./tests/data/pdf/2206.01062.pdf")
# Explicitly set the accelerator
# accelerator_options = AcceleratorOptions(
@ -30,6 +30,9 @@ def main():
# num_threads=8, device=AcceleratorDevice.CUDA
# )
# easyocr doesnt support cuda:N allocation, defaults to cuda:0
# accelerator_options = AcceleratorOptions(num_threads=8, device="cuda:1")
pipeline_options = PdfPipelineOptions()
pipeline_options.accelerator_options = accelerator_options
pipeline_options.do_ocr = True

6
docs/examples/run_with_formats.py
View File

@ -25,9 +25,8 @@ def main():
Path("tests/data/docx/lorem_ipsum.docx"),
Path("tests/data/pptx/powerpoint_sample.pptx"),
Path("tests/data/2305.03393v1-pg9-img.png"),
Path("tests/data/2206.01062.pdf"),
Path("tests/data/test_01.asciidoc"),
Path("tests/data/test_01.asciidoc"),
Path("tests/data/pdf/2206.01062.pdf"),
Path("tests/data/asciidoc/test_01.asciidoc"),
]
## for defaults use:
@ -44,6 +43,7 @@ def main():
InputFormat.HTML,
InputFormat.PPTX,
InputFormat.ASCIIDOC,
InputFormat.CSV,
InputFormat.MD,
], # whitelist formats, non-matching files are ignored.
format_options={

2
docs/examples/tesseract_lang_detection.py
View File

@ -10,7 +10,7 @@ from docling.document_converter import DocumentConverter, PdfFormatOption
def main():
input_doc = Path("./tests/data/2206.01062.pdf")
input_doc = Path("./tests/data/pdf/2206.01062.pdf")
# Set lang=["auto"] with a tesseract OCR engine: TesseractOcrOptions, TesseractCliOcrOptions
# ocr_options = TesseractOcrOptions(lang=["auto"])

2
docs/examples/translate.py
View File

@ -32,7 +32,7 @@ def translate(text: str, src: str = "en", dest: str = "de"):
def main():
logging.basicConfig(level=logging.INFO)
input_doc_path = Path("./tests/data/2206.01062.pdf")
input_doc_path = Path("./tests/data/pdf/2206.01062.pdf")
output_dir = Path("scratch")
# Important: For operating with page images, we must keep them, otherwise the DocumentConverter

41
docs/faq.md → docs/faq/index.md
View File

@ -132,9 +132,48 @@ This is a collection of FAQ collected from the user questions on <https://github
```
??? Some images are missing from MS Word and Powerpoint"
??? question "Some images are missing from MS Word and Powerpoint"
### Some images are missing from MS Word and Powerpoint
The image processing library used by Docling is able to handle embedded WMF images only on Windows platform.
If you are on other operaring systems, these images will be ignored.
??? question "`HybridChunker` triggers warning: 'Token indices sequence length is longer than the specified maximum sequence length for this model'"
### `HybridChunker` triggers warning: 'Token indices sequence length is longer than the specified maximum sequence length for this model'
**TLDR**:
In the context of the `HybridChunker`, this is a known & ancitipated "false alarm".
**Details**:
Using the [`HybridChunker`](../concepts/chunking.md#hybrid-chunker) often triggers a warning like this:
> Token indices sequence length is longer than the specified maximum sequence length for this model (531 > 512). Running this sequence through the model will result in indexing errors
This is a warning that is emitted by transformers, saying that actually *running this sequence through the model* will result in indexing errors, i.e. the problematic case is only if one indeed passes the particular sequence through the (embedding) model.
In our case though, this occurs as a "false alarm", since what happens is the following:
- the chunker invokes the tokenizer on a potentially long sequence (e.g. 530 tokens as mentioned in the warning) in order to count its tokens, i.e. to assess if it is short enough. At this point transformers already emits the warning above!
- whenever the sequence at hand is oversized, the chunker proceeds to split it (but the transformers warning has already been shown nonetheless)
What is important is the actual token length of the produced chunks.
The snippet below can be used for getting the actual maximum chunk size (for users wanting to confirm that this does not exceed the model limit):
```python
chunk_max_len = 0
for i, chunk in enumerate(chunks):
ser_txt = chunker.serialize(chunk=chunk)
ser_tokens = len(tokenizer.tokenize(ser_txt))
if ser_tokens > chunk_max_len:
chunk_max_len = ser_tokens
print(f"{i}\t{ser_tokens}\t{repr(ser_txt[:100])}...")
print(f"Longest chunk yielded: {chunk_max_len} tokens")
print(f"Model max length: {tokenizer.model_max_length}")
```
Also see [docling#725](https://github.com/DS4SD/docling/issues/725).
Source: Issue [docling-core#119](https://github.com/DS4SD/docling-core/issues/119)

2
docs/index.md
View File

@ -47,6 +47,6 @@ Docling simplifies document processing, parsing diverse formats — including ad
Docling has been brought to you by IBM.
[supported_formats]: ./supported_formats.md
[supported_formats]: ./usage/supported_formats.md
[docling_document]: ./concepts/docling_document.md
[integrations]: ./integrations/index.md

0
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216
docs/usage/enrichments.md Normal file
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@ -0,0 +1,216 @@
Docling allows to enrich the conversion pipeline with additional steps which process specific document components,
e.g. code blocks, pictures, etc. The extra steps usually require extra models executions which may increase
the processing time consistently. For this reason most enrichment models are disabled by default.
The following table provides an overview of the default enrichment models available in Docling.
| Feature | Parameter | Processed item | Description |
| ------- | --------- | ---------------| ----------- |
| Code understanding | `do_code_enrichment` | `CodeItem` | See [docs below](#code-understanding). |
| Formula understanding | `do_formula_enrichment` | `TextItem` with label `FORMULA` | See [docs below](#formula-understanding). |
| Picrure classification | `do_picture_classification` | `PictureItem` | See [docs below](#picture-classification). |
| Picture description | `do_picture_description` | `PictureItem` | See [docs below](#picture-description). |
## Enrichments details
### Code understanding
The code understanding step allows to use advance parsing for code blocks found in the document.
This enrichment model also set the `code_language` property of the `CodeItem`.
Model specs: see the [`CodeFormula` model card](https://huggingface.co/ds4sd/CodeFormula).
Example command line:
```sh
docling --enrich-code FILE
```
Example code:
```py
from docling.document_converter import DocumentConverter, PdfFormatOption
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.datamodel.base_models import InputFormat
pipeline_options = PdfPipelineOptions()
pipeline_options.do_code_enrichment = True
converter = DocumentConverter(format_options={
InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
})
result = converter.convert("https://arxiv.org/pdf/2501.17887")
doc = result.document
```
### Formula understanding
The formula understanding step will analize the equation formulas in documents and extract their LaTeX representation.
The HTML export functions in the DoclingDocument will leverage the formula and visualize the result using the mathml html syntax.
Model specs: see the [`CodeFormula` model card](https://huggingface.co/ds4sd/CodeFormula).
Example command line:
```sh
docling --enrich-formula FILE
```
Example code:
```py
from docling.document_converter import DocumentConverter, PdfFormatOption
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.datamodel.base_models import InputFormat
pipeline_options = PdfPipelineOptions()
pipeline_options.do_formula_enrichment = True
converter = DocumentConverter(format_options={
InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
})
result = converter.convert("https://arxiv.org/pdf/2501.17887")
doc = result.document
```
### Picture classification
The picture classification step classifies the `PictureItem` elements in the document with the `DocumentFigureClassifier` model.
This model is specialized to understand the classes of pictures found in documents, e.g. different chart types, flow diagrams,
logos, signatures, etc.
Model specs: see the [`DocumentFigureClassifier` model card](https://huggingface.co/ds4sd/DocumentFigureClassifier).
Example command line:
```sh
docling --enrich-picture-classes FILE
```
Example code:
```py
from docling.document_converter import DocumentConverter, PdfFormatOption
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.datamodel.base_models import InputFormat
pipeline_options = PdfPipelineOptions()
pipeline_options.generate_picture_images = True
pipeline_options.images_scale = 2
pipeline_options.do_picture_classification = True
converter = DocumentConverter(format_options={
InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
})
result = converter.convert("https://arxiv.org/pdf/2501.17887")
doc = result.document
```
### Picture description
The picture description step allows to annotate a picture with a vision model. This is also known as a "captioning" task.
The Docling pipeline allows to load and run models completely locally as well as connecting to remote API which support the chat template.
Below follow a few examples on how to use some common vision model and remote services.
```py
from docling.document_converter import DocumentConverter, PdfFormatOption
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.datamodel.base_models import InputFormat
pipeline_options = PdfPipelineOptions()
pipeline_options.do_picture_description = True
converter = DocumentConverter(format_options={
InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
})
result = converter.convert("https://arxiv.org/pdf/2501.17887")
doc = result.document
```
#### Granite Vision model
Model specs: see the [`ibm-granite/granite-vision-3.1-2b-preview` model card](https://huggingface.co/ibm-granite/granite-vision-3.1-2b-preview).
Usage in Docling:
```py
from docling.datamodel.pipeline_options import granite_picture_description
pipeline_options.picture_description_options = granite_picture_description
```
#### SmolVLM model
Model specs: see the [`HuggingFaceTB/SmolVLM-256M-Instruct` model card](https://huggingface.co/HuggingFaceTB/SmolVLM-256M-Instruct).
Usage in Docling:
```py
from docling.datamodel.pipeline_options import smolvlm_picture_description
pipeline_options.picture_description_options = smolvlm_picture_description
```
#### Other vision models
The option class `PictureDescriptionVlmOptions` allows to use any another model from the Hugging Face Hub.
```py
from docling.datamodel.pipeline_options import PictureDescriptionVlmOptions
pipeline_options.picture_description_options = PictureDescriptionVlmOptions(
repo_id="", # <-- add here the Hugging Face repo_id of your favorite VLM
prompt="Describe the image in three sentences. Be consise and accurate.",
)
```
#### Remote vision model
The option class `PictureDescriptionApiOptions` allows to use models hosted on remote platforms, e.g.
on local endpoints served by [VLLM](https://docs.vllm.ai), [Ollama](https://ollama.com/) and others,
or cloud providers like [IBM watsonx.ai](https://www.ibm.com/products/watsonx-ai), etc.
_Note: in most cases this option will send your data to the remote service provider._
Usage in Docling:
```py
from docling.datamodel.pipeline_options import PictureDescriptionApiOptions
# Enable connections to remote services
pipeline_options.enable_remote_services=True # <-- this is required!
# Example using a model running locally, e.g. via VLLM
# $ vllm serve MODEL_NAME
pipeline_options.picture_description_options = PictureDescriptionApiOptions(
url="http://localhost:8000/v1/chat/completions",
params=dict(
model="MODEL NAME",
seed=42,
max_completion_tokens=200,
),
prompt="Describe the image in three sentences. Be consise and accurate.",
timeout=90,
)
```
End-to-end code snippets for cloud providers are available in the examples section:
- [IBM watsonx.ai](../examples/pictures_description_api.py)
## Develop new enrichment models
Beside looking at the implementation of all the models listed above, the Docling documentation has a few examples
dedicated to the implementation of enrichment models.
- [Develop picture enrichment](../examples/develop_picture_enrichment.py)
- [Develop formula enrichment](../examples/develop_formula_understanding.py)

113
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@ -22,16 +22,91 @@ A simple example would look like this:
docling https://arxiv.org/pdf/2206.01062
```
To see all available options (export formats etc.) run `docling --help`. More details in the [CLI reference page](./reference/cli.md).
To see all available options (export formats etc.) run `docling --help`. More details in the [CLI reference page](../reference/cli.md).
### Advanced options
#### Model prefetching and offline usage
By default, models are downloaded automatically upon first usage. If you would prefer
to explicitly prefetch them for offline use (e.g. in air-gapped environments) you can do
that as follows:
**Step 1: Prefetch the models**
Use the `docling-tools models download` utility:
```sh
$ docling-tools models download
Downloading layout model...
Downloading tableformer model...
Downloading picture classifier model...
Downloading code formula model...
Downloading easyocr models...
Models downloaded into $HOME/.cache/docling/models.
```
Alternatively, models can be programmatically downloaded using `docling.utils.model_downloader.download_models()`.
**Step 2: Use the prefetched models**
```python
from docling.datamodel.base_models import InputFormat
from docling.datamodel.pipeline_options import EasyOcrOptions, PdfPipelineOptions
from docling.document_converter import DocumentConverter, PdfFormatOption
artifacts_path = "/local/path/to/models"
pipeline_options = PdfPipelineOptions(artifacts_path=artifacts_path)
doc_converter = DocumentConverter(
format_options={
InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
}
)
```
Or using the CLI:
```sh
docling --artifacts-path="/local/path/to/models" FILE
```
#### Using remote services
The main purpose of Docling is to run local models which are not sharing any user data with remote services.
Anyhow, there are valid use cases for processing part of the pipeline using remote services, for example invoking OCR engines from cloud vendors or the usage of hosted LLMs.
In Docling we decided to allow such models, but we require the user to explicitly opt-in in communicating with external services.
```py
from docling.datamodel.base_models import InputFormat
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.document_converter import DocumentConverter, PdfFormatOption
pipeline_options = PdfPipelineOptions(enable_remote_services=True)
doc_converter = DocumentConverter(
format_options={
InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
}
)
```
When the value `enable_remote_services=True` is not set, the system will raise an exception `OperationNotAllowed()`.
_Note: This option is only related to the system sending user data to remote services. Control of pulling data (e.g. model weights) follows the logic described in [Model prefetching and offline usage](#model-prefetching-and-offline-usage)._
##### List of remote model services
The options in this list require the explicit `enable_remote_services=True` when processing the documents.
- `PictureDescriptionApiOptions`: Using vision models via API calls.
#### Adjust pipeline features
The example file [custom_convert.py](./examples/custom_convert.py) contains multiple ways
The example file [custom_convert.py](../examples/custom_convert.py) contains multiple ways
one can adjust the conversion pipeline and features.
##### Control PDF table extraction options
You can control if table structure recognition should map the recognized structure back to PDF cells (default) or use text cells from the structure prediction itself.
@ -70,28 +145,6 @@ doc_converter = DocumentConverter(
)
```
##### Provide specific artifacts path
By default, artifacts such as models are downloaded automatically upon first usage. If you would prefer to use a local path where the artifacts have been explicitly prefetched, you can do that as follows:
```python
from docling.datamodel.base_models import InputFormat
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling.document_converter import DocumentConverter, PdfFormatOption
from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline
# # to explicitly prefetch:
# artifacts_path = StandardPdfPipeline.download_models_hf()
artifacts_path = "/local/path/to/artifacts"
pipeline_options = PdfPipelineOptions(artifacts_path=artifacts_path)
doc_converter = DocumentConverter(
format_options={
InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
}
)
```
#### Impose limits on the document size
@ -130,13 +183,13 @@ You can limit the CPU threads used by Docling by setting the environment variabl
!!! note
This section discusses directly invoking a [backend](./concepts/architecture.md),
This section discusses directly invoking a [backend](../concepts/architecture.md),
i.e. using a low-level API. This should only be done when necessary. For most cases,
using a `DocumentConverter` (high-level API) as discussed in the sections above
should suffice — and is the recommended way.
By default, Docling will try to identify the document format to apply the appropriate conversion backend (see the list of [supported formats](./supported_formats.md)).
You can restrict the `DocumentConverter` to a set of allowed document formats, as shown in the [Multi-format conversion](./examples/run_with_formats.py) example.
By default, Docling will try to identify the document format to apply the appropriate conversion backend (see the list of [supported formats](../supported_formats.md)).
You can restrict the `DocumentConverter` to a set of allowed document formats, as shown in the [Multi-format conversion](../examples/run_with_formats.py) example.
Alternatively, you can also use the specific backend that matches your document content. For instance, you can use `HTMLDocumentBackend` for HTML pages:
```python
@ -161,9 +214,9 @@ print(dl_doc.export_to_markdown())
## Chunking
You can chunk a Docling document using a [chunker](concepts/chunking.md), such as a
You can chunk a Docling document using a [chunker](../concepts/chunking.md), such as a
`HybridChunker`, as shown below (for more details check out
[this example](examples/hybrid_chunking.ipynb)):
[this example](../examples/hybrid_chunking.ipynb)):
```python
from docling.document_converter import DocumentConverter

7
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@ -1,6 +1,6 @@
Docling can parse various documents formats into a unified representation (Docling
Document), which it can export to different formats too — check out
[Architecture](./concepts/architecture.md) for more details.
[Architecture](../concepts/architecture.md) for more details.
Below you can find a listing of all supported input and output formats.
@ -13,6 +13,7 @@ Below you can find a listing of all supported input and output formats.
| Markdown | |
| AsciiDoc | |
| HTML, XHTML | |
| CSV | |
| PNG, JPEG, TIFF, BMP | Image formats |
Schema-specific support:
@ -20,8 +21,8 @@ Schema-specific support:
| Format | Description |
|--------|-------------|
| USPTO XML | XML format followed by [USPTO](https://www.uspto.gov/patents) patents |
| PMC XML | XML format followed by [PubMed Central®](https://pmc.ncbi.nlm.nih.gov/) articles |
| Docling JSON | JSON-serialized [Docling Document](./concepts/docling_document.md) |
| JATS XML | XML format followed by [JATS](https://jats.nlm.nih.gov/) articles |
| Docling JSON | JSON-serialized [Docling Document](../concepts/docling_document.md) |
## Supported output formats

10
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@ -117,12 +117,12 @@ conv_result: ConversionResult = doc_converter.convert("https://arxiv.org/pdf/240
## Convert several files at once:
input_files = [
"tests/data/wiki_duck.html",
"tests/data/word_sample.docx",
"tests/data/lorem_ipsum.docx",
"tests/data/powerpoint_sample.pptx",
"tests/data/html/wiki_duck.html",
"tests/data/docx/word_sample.docx",
"tests/data/docx/lorem_ipsum.docx",
"tests/data/pptx/powerpoint_sample.pptx",
"tests/data/2305.03393v1-pg9-img.png",
"tests/data/2206.01062.pdf",
"tests/data/pdf/2206.01062.pdf",
]
# Directly pass list of files or streams to `convert_all`

22
mkdocs.yml
View File

@ -54,11 +54,14 @@ theme:
nav:
- Home:
- "Docling": index.md
- Installation: installation.md
- Usage: usage.md
- Supported formats: supported_formats.md
- FAQ: faq.md
- Docling v2: v2.md
- Installation:
- Installation: installation/index.md
- Usage:
- Usage: usage/index.md
- Supported formats: usage/supported_formats.md
- Enrichment features: usage/enrichments.md
- FAQ:
- FAQ: faq/index.md
- Concepts:
- Concepts: concepts/index.md
- Architecture: concepts/architecture.md
@ -72,13 +75,14 @@ nav:
- "Batch conversion": examples/batch_convert.py
- "Multi-format conversion": examples/run_with_formats.py
- "Figure export": examples/export_figures.py
- "Figure enrichment": examples/develop_picture_enrichment.py
- "Table export": examples/export_tables.py
- "Multimodal export": examples/export_multimodal.py
- "Force full page OCR": examples/full_page_ocr.py
- "Automatic OCR language detection with tesseract": examples/tesseract_lang_detection.py
- "RapidOCR with custom OCR models": examples/rapidocr_with_custom_models.py
- "Accelerator options": examples/run_with_accelerator.py
- "Simple translation": examples/translate.py
- examples/backend_csv.ipynb
- examples/backend_xml_rag.ipynb
- ✂️ Chunking:
- examples/hybrid_chunking.ipynb
@ -86,6 +90,12 @@ nav:
- examples/rag_haystack.ipynb
- examples/rag_langchain.ipynb
- examples/rag_llamaindex.ipynb
- 🖼️ Picture annotation:
- "Annotate picture with local VLM": examples/pictures_description.ipynb
- "Annotate picture with remote VLM": examples/pictures_description_api.py
- ✨ Enrichment development:
- "Figure enrichment": examples/develop_picture_enrichment.py
- "Formula enrichment": examples/develop_formula_understanding.py
- 🗂️ More examples:
- examples/rag_weaviate.ipynb
- RAG with Granite [↗]: https://github.com/ibm-granite-community/granite-snack-cookbook/blob/main/recipes/RAG/Granite_Docling_RAG.ipynb

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28
pyproject.toml
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@ -1,6 +1,6 @@
[tool.poetry]
name = "docling"
version = "2.17.0" # DO NOT EDIT, updated automatically
version = "2.25.1" # DO NOT EDIT, updated automatically
description = "SDK and CLI for parsing PDF, DOCX, HTML, and more, to a unified document representation for powering downstream workflows such as gen AI applications."
authors = ["Christoph Auer <cau@zurich.ibm.com>", "Michele Dolfi <dol@zurich.ibm.com>", "Maxim Lysak <mly@zurich.ibm.com>", "Nikos Livathinos <nli@zurich.ibm.com>", "Ahmed Nassar <ahn@zurich.ibm.com>", "Panos Vagenas <pva@zurich.ibm.com>", "Peter Staar <taa@zurich.ibm.com>"]
license = "MIT"
@ -26,10 +26,9 @@ packages = [{include = "docling"}]
######################
python = "^3.9"
pydantic = "^2.0.0"
docling-core = {version = "^2.16.1", extras = ["chunking"]}
docling-ibm-models = "^3.3.0"
deepsearch-glm = "^1.0.0"
docling-parse = "^3.1.0"
docling-core = {extras = ["chunking"], version = "^2.19.0"}
docling-ibm-models = "^3.4.0"
docling-parse = "^3.3.0"
filetype = "^1.2.0"
pypdfium2 = "^4.30.0"
pydantic-settings = "^2.3.0"
@ -59,8 +58,16 @@ onnxruntime = [
{ version = ">=1.7.0,<1.20.0", optional = true, markers = "python_version < '3.10'" },
{ version = "^1.7.0", optional = true, markers = "python_version >= '3.10'" }
]
pillow = "^10.0.0"
pylatexenc = "^2.10"
transformers = [
{markers = "sys_platform != 'darwin' or platform_machine != 'x86_64'", version = "^4.46.0", optional = true },
{markers = "sys_platform == 'darwin' and platform_machine == 'x86_64'", version = "~4.42.0", optional = true }
]
accelerate = [
{markers = "sys_platform != 'darwin' or platform_machine != 'x86_64'", version = "^1.2.1", optional = true },
]
pillow = ">=10.0.0,<12.0.0"
tqdm = "^4.65.0"
[tool.poetry.group.dev.dependencies]
black = {extras = ["jupyter"], version = "^24.4.2"}
@ -80,6 +87,7 @@ ipykernel = "^6.29.5"
ipywidgets = "^8.1.5"
nbqa = "^1.9.0"
types-openpyxl = "^3.1.5.20241114"
types-tqdm = "^4.67.0.20241221"
[tool.poetry.group.docs.dependencies]
mkdocs-material = "^9.5.40"
@ -120,10 +128,12 @@ torchvision = [
[tool.poetry.extras]
tesserocr = ["tesserocr"]
ocrmac = ["ocrmac"]
vlm = ["transformers", "accelerate"]
rapidocr = ["rapidocr-onnxruntime", "onnxruntime"]
[tool.poetry.scripts]
docling = "docling.cli.main:app"
docling-tools = "docling.cli.tools:app"
[build-system]
requires = ["poetry-core"]
@ -157,11 +167,9 @@ module = [
"docling_ibm_models.*",
"easyocr.*",
"ocrmac.*",
"deepsearch_glm.*",
"lxml.*",
"bs4.*",
"huggingface_hub.*",
"pylatexenc.*"
"transformers.*",
]
ignore_missing_imports = true

0
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0
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5
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@ -0,0 +1,5 @@
1,2,3,4
a,b,c,d
a,",",c,d
a,b,c,d
a,b,c,d
1 1 2 3 4
2 a b c d
3 a , c d
4 a b c d
5 a b c d

6
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View File

@ -0,0 +1,6 @@
Index,Customer Id,First Name,Last Name,Company,City,Country,Phone 1,Phone 2,Email,Subscription Date,Website
1,DD37Cf93aecA6Dc,Sheryl,Baxter,Rasmussen Group,East Leonard,Chile,229.077.5154,397.884.0519x718,zunigavanessa@smith.info,2020-08-24,http://www.stephenson.com/
2,1Ef7b82A4CAAD10,Preston,"Lozano, Dr",Vega-Gentry,East Jimmychester,Djibouti,5153435776,686-620-1820x944,vmata@colon.com,2021-04-23,http://www.hobbs.com/
3,6F94879bDAfE5a6,Roy,Berry,Murillo-Perry,Isabelborough,Antigua and Barbuda,+1-539-402-0259,(496)978-3969x58947,beckycarr@hogan.com,2020-03-25,http://www.lawrence.com/
4,5Cef8BFA16c5e3c,Linda,Olsen,"Dominguez, Mcmillan and Donovan",Bensonview,Dominican Republic,001-808-617-6467x12895,+1-813-324-8756,stanleyblackwell@benson.org,2020-06-02,http://www.good-lyons.com/
5,053d585Ab6b3159,Joanna,Bender,"Martin, Lang and Andrade",West Priscilla,Slovakia (Slovak Republic),001-234-203-0635x76146,001-199-446-3860x3486,colinalvarado@miles.net,2021-04-17,https://goodwin-ingram.com/
1 Index Customer Id First Name Last Name Company City Country Phone 1 Phone 2 Email Subscription Date Website
2 1 DD37Cf93aecA6Dc Sheryl Baxter Rasmussen Group East Leonard Chile 229.077.5154 397.884.0519x718 zunigavanessa@smith.info 2020-08-24 http://www.stephenson.com/
3 2 1Ef7b82A4CAAD10 Preston Lozano, Dr Vega-Gentry East Jimmychester Djibouti 5153435776 686-620-1820x944 vmata@colon.com 2021-04-23 http://www.hobbs.com/
4 3 6F94879bDAfE5a6 Roy Berry Murillo-Perry Isabelborough Antigua and Barbuda +1-539-402-0259 (496)978-3969x58947 beckycarr@hogan.com 2020-03-25 http://www.lawrence.com/
5 4 5Cef8BFA16c5e3c Linda Olsen Dominguez, Mcmillan and Donovan Bensonview Dominican Republic 001-808-617-6467x12895 +1-813-324-8756 stanleyblackwell@benson.org 2020-06-02 http://www.good-lyons.com/
6 5 053d585Ab6b3159 Joanna Bender Martin, Lang and Andrade West Priscilla Slovakia (Slovak Republic) 001-234-203-0635x76146 001-199-446-3860x3486 colinalvarado@miles.net 2021-04-17 https://goodwin-ingram.com/

5
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@ -0,0 +1,5 @@
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a,b,c,d
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5 a,b,c,d

6
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@ -0,0 +1,6 @@
Index|Customer Id|First Name|Last Name|Company|City|Country|Phone 1|Phone 2|Email|Subscription Date|Website
1|DD37Cf93aecA6Dc|Sheryl|Baxter|Rasmussen Group|East Leonard|Chile|229.077.5154|397.884.0519x718|zunigavanessa@smith.info|2020-08-24|http://www.stephenson.com/
2|1Ef7b82A4CAAD10|Preston|Lozano|Vega-Gentry|East Jimmychester|Djibouti|5153435776|686-620-1820x944|vmata@colon.com|2021-04-23|http://www.hobbs.com/
3|6F94879bDAfE5a6|Roy|Berry|Murillo-Perry|Isabelborough|Antigua and Barbuda|+1-539-402-0259|(496)978-3969x58947|beckycarr@hogan.com|2020-03-25|http://www.lawrence.com/
4|5Cef8BFA16c5e3c|Linda|Olsen|"Dominguez|Mcmillan and Donovan"|Bensonview|Dominican Republic|001-808-617-6467x12895|+1-813-324-8756|stanleyblackwell@benson.org|2020-06-02|http://www.good-lyons.com/
5|053d585Ab6b3159|Joanna|Bender|"Martin|Lang and Andrade"|West Priscilla|Slovakia (Slovak Republic)|001-234-203-0635x76146|001-199-446-3860x3486|colinalvarado@miles.net|2021-04-17|https://goodwin-ingram.com/
1 Index Customer Id First Name Last Name Company City Country Phone 1 Phone 2 Email Subscription Date Website
2 1 DD37Cf93aecA6Dc Sheryl Baxter Rasmussen Group East Leonard Chile 229.077.5154 397.884.0519x718 zunigavanessa@smith.info 2020-08-24 http://www.stephenson.com/
3 2 1Ef7b82A4CAAD10 Preston Lozano Vega-Gentry East Jimmychester Djibouti 5153435776 686-620-1820x944 vmata@colon.com 2021-04-23 http://www.hobbs.com/
4 3 6F94879bDAfE5a6 Roy Berry Murillo-Perry Isabelborough Antigua and Barbuda +1-539-402-0259 (496)978-3969x58947 beckycarr@hogan.com 2020-03-25 http://www.lawrence.com/
5 4 5Cef8BFA16c5e3c Linda Olsen Dominguez|Mcmillan and Donovan Bensonview Dominican Republic 001-808-617-6467x12895 +1-813-324-8756 stanleyblackwell@benson.org 2020-06-02 http://www.good-lyons.com/
6 5 053d585Ab6b3159 Joanna Bender Martin|Lang and Andrade West Priscilla Slovakia (Slovak Republic) 001-234-203-0635x76146 001-199-446-3860x3486 colinalvarado@miles.net 2021-04-17 https://goodwin-ingram.com/

6
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@ -0,0 +1,6 @@
Index;Customer Id;First Name;Last Name;Company;City;Country;Phone 1;Phone 2;Email;Subscription Date;Website
1;DD37Cf93aecA6Dc;Sheryl;Baxter;Rasmussen Group;East Leonard;Chile;229.077.5154;397.884.0519x718;zunigavanessa@smith.info;2020-08-24;http://www.stephenson.com/
2;1Ef7b82A4CAAD10;Preston;Lozano;Vega-Gentry;East Jimmychester;Djibouti;5153435776;686-620-1820x944;vmata@colon.com;2021-04-23;http://www.hobbs.com/
3;6F94879bDAfE5a6;Roy;Berry;Murillo-Perry;Isabelborough;Antigua and Barbuda;+1-539-402-0259;(496)978-3969x58947;beckycarr@hogan.com;2020-03-25;http://www.lawrence.com/
4;5Cef8BFA16c5e3c;Linda;Olsen;"Dominguez;Mcmillan and Donovan";Bensonview;Dominican Republic;001-808-617-6467x12895;+1-813-324-8756;stanleyblackwell@benson.org;2020-06-02;http://www.good-lyons.com/
5;053d585Ab6b3159;Joanna;Bender;"Martin;Lang and Andrade";West Priscilla;Slovakia (Slovak Republic);001-234-203-0635x76146;001-199-446-3860x3486;colinalvarado@miles.net;2021-04-17;https://goodwin-ingram.com/
1 Index Customer Id First Name Last Name Company City Country Phone 1 Phone 2 Email Subscription Date Website
2 1 DD37Cf93aecA6Dc Sheryl Baxter Rasmussen Group East Leonard Chile 229.077.5154 397.884.0519x718 zunigavanessa@smith.info 2020-08-24 http://www.stephenson.com/
3 2 1Ef7b82A4CAAD10 Preston Lozano Vega-Gentry East Jimmychester Djibouti 5153435776 686-620-1820x944 vmata@colon.com 2021-04-23 http://www.hobbs.com/
4 3 6F94879bDAfE5a6 Roy Berry Murillo-Perry Isabelborough Antigua and Barbuda +1-539-402-0259 (496)978-3969x58947 beckycarr@hogan.com 2020-03-25 http://www.lawrence.com/
5 4 5Cef8BFA16c5e3c Linda Olsen Dominguez;Mcmillan and Donovan Bensonview Dominican Republic 001-808-617-6467x12895 +1-813-324-8756 stanleyblackwell@benson.org 2020-06-02 http://www.good-lyons.com/
6 5 053d585Ab6b3159 Joanna Bender Martin;Lang and Andrade West Priscilla Slovakia (Slovak Republic) 001-234-203-0635x76146 001-199-446-3860x3486 colinalvarado@miles.net 2021-04-17 https://goodwin-ingram.com/

6
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@ -0,0 +1,6 @@
Index Customer Id First Name Last Name Company City Country Phone 1 Phone 2 Email Subscription Date Website
1 DD37Cf93aecA6Dc Sheryl Baxter Rasmussen Group East Leonard Chile 229.077.5154 397.884.0519x718 zunigavanessa@smith.info 2020-08-24 http://www.stephenson.com/
2 1Ef7b82A4CAAD10 Preston Lozano Vega-Gentry East Jimmychester Djibouti 5153435776 686-620-1820x944 vmata@colon.com 2021-04-23 http://www.hobbs.com/
3 6F94879bDAfE5a6 Roy Berry Murillo-Perry Isabelborough Antigua and Barbuda +1-539-402-0259 (496)978-3969x58947 beckycarr@hogan.com 2020-03-25 http://www.lawrence.com/
4 5Cef8BFA16c5e3c Linda Olsen "Dominguez Mcmillan and Donovan" Bensonview Dominican Republic 001-808-617-6467x12895 +1-813-324-8756 stanleyblackwell@benson.org 2020-06-02 http://www.good-lyons.com/
5 053d585Ab6b3159 Joanna Bender "Martin Lang and Andrade" West Priscilla Slovakia (Slovak Republic) 001-234-203-0635x76146 001-199-446-3860x3486 colinalvarado@miles.net 2021-04-17 https://goodwin-ingram.com/
1 Index Customer Id First Name Last Name Company City Country Phone 1 Phone 2 Email Subscription Date Website
2 1 DD37Cf93aecA6Dc Sheryl Baxter Rasmussen Group East Leonard Chile 229.077.5154 397.884.0519x718 zunigavanessa@smith.info 2020-08-24 http://www.stephenson.com/
3 2 1Ef7b82A4CAAD10 Preston Lozano Vega-Gentry East Jimmychester Djibouti 5153435776 686-620-1820x944 vmata@colon.com 2021-04-23 http://www.hobbs.com/
4 3 6F94879bDAfE5a6 Roy Berry Murillo-Perry Isabelborough Antigua and Barbuda +1-539-402-0259 (496)978-3969x58947 beckycarr@hogan.com 2020-03-25 http://www.lawrence.com/
5 4 5Cef8BFA16c5e3c Linda Olsen Dominguez Mcmillan and Donovan Bensonview Dominican Republic 001-808-617-6467x12895 +1-813-324-8756 stanleyblackwell@benson.org 2020-06-02 http://www.good-lyons.com/
6 5 053d585Ab6b3159 Joanna Bender Martin Lang and Andrade West Priscilla Slovakia (Slovak Republic) 001-234-203-0635x76146 001-199-446-3860x3486 colinalvarado@miles.net 2021-04-17 https://goodwin-ingram.com/

5
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1,2,3,4
a,'b',c,d
a,b,c
a,b,c,d
a,b,c,d
1 1,2,3,4
2 a,'b',c,d
3 a,b,c
4 a,b,c,d
5 a,b,c,d

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a,b,c,d
a,b,c,d,e
a,b,c,d
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4 a,b,c,d
5 a,b,c,d

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<paragraph><location><page_1><loc_34><loc_77><loc_62><loc_78></location>{ ahn,nli,mly,taa } @zurich.ibm.com</paragraph>
<subtitle-level-1><location><page_1><loc_24><loc_71><loc_31><loc_73></location>Abstract</subtitle-level-1>
<subtitle-level-1><location><page_1><loc_52><loc_71><loc_67><loc_72></location>a. Picture of a table:</subtitle-level-1>
<paragraph><location><page_1><loc_8><loc_35><loc_47><loc_70></location>Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.</paragraph>
<subtitle-level-1><location><page_1><loc_8><loc_30><loc_21><loc_32></location>1. Introduction</subtitle-level-1>
<paragraph><location><page_1><loc_8><loc_10><loc_47><loc_29></location>The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues.</paragraph>
<figure>
<location><page_1><loc_52><loc_62><loc_88><loc_71></location>
</figure>
<caption><location><page_1><loc_8><loc_35><loc_47><loc_70></location>Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.</caption>
<table>
<location><page_1><loc_52><loc_62><loc_88><loc_71></location>
<caption>Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.</caption>
<row_0><col_0><col_header>3</col_0><col_1><col_header>1</col_1></row_0>
</table>
<paragraph><location><page_1><loc_52><loc_58><loc_79><loc_60></location>- b. Red-annotation of bounding boxes, Blue-predictions by TableFormer</paragraph>
<paragraph><location><page_1><loc_52><loc_46><loc_80><loc_47></location>- c. Structure predicted by TableFormer:</paragraph>
<figure>
<location><page_1><loc_51><loc_48><loc_88><loc_57></location>
</figure>
<paragraph><location><page_1><loc_52><loc_46><loc_80><loc_47></location>- c. Structure predicted by TableFormer:</paragraph>
<figure>
<location><page_1><loc_52><loc_37><loc_88><loc_45></location>
<caption>Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.</caption>
</figure>
<caption><location><page_1><loc_50><loc_29><loc_89><loc_35></location>Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.</caption>
<table>
<location><page_1><loc_52><loc_37><loc_88><loc_45></location>
<caption>Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.</caption>
<row_0><col_0><col_header>0</col_0><col_1><col_header>1</col_1><col_2><col_header>1</col_2><col_3><col_header>2 1</col_3><col_4><col_header>2 1</col_4><col_5><body></col_5></row_0>
<row_1><col_0><body>3</col_0><col_1><body>4</col_1><col_2><body>5 3</col_2><col_3><body>6</col_3><col_4><body>7</col_4><col_5><body></col_5></row_1>
<row_2><col_0><body>8</col_0><col_1><body>9</col_1><col_2><body>10</col_2><col_3><body>11</col_3><col_4><body>12</col_4><col_5><body>2</col_5></row_2>
@ -55,7 +53,6 @@
<paragraph><location><page_3><loc_8><loc_21><loc_47><loc_38></location>Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered.</paragraph>
<subtitle-level-1><location><page_3><loc_8><loc_18><loc_17><loc_20></location>3. Datasets</subtitle-level-1>
<paragraph><location><page_3><loc_8><loc_10><loc_47><loc_17></location>We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im-</paragraph>
<caption><location><page_3><loc_50><loc_64><loc_89><loc_66></location>Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets</caption>
<figure>
<location><page_3><loc_51><loc_68><loc_90><loc_90></location>
<caption>Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets</caption>
@ -68,7 +65,6 @@
<paragraph><location><page_4><loc_8><loc_45><loc_47><loc_60></location>As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data.</paragraph>
<paragraph><location><page_4><loc_8><loc_21><loc_47><loc_45></location>Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain.</paragraph>
<paragraph><location><page_4><loc_8><loc_10><loc_47><loc_20></location>In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third</paragraph>
<caption><location><page_4><loc_50><loc_72><loc_89><loc_79></location>Table 1: Both "Combined-Tabnet" and "CombinedTabnet" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank.</caption>
<table>
<location><page_4><loc_51><loc_80><loc_89><loc_91></location>
<caption>Table 1: Both "Combined-Tabnet" and "CombinedTabnet" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank.</caption>
@ -80,6 +76,7 @@
<row_5><col_0><row_header>Combined(**)</col_0><col_1><body>3</col_1><col_2><body>3</col_2><col_3><body>500k</col_3><col_4><body>PNG</col_4></row_5>
<row_6><col_0><row_header>SynthTabNet</col_0><col_1><body>3</col_1><col_2><body>3</col_2><col_3><body>600k</col_3><col_4><body>PNG</col_4></row_6>
</table>
<caption><location><page_4><loc_50><loc_72><loc_89><loc_79></location>Table 1: Both "Combined-Tabnet" and "CombinedTabnet" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank.</caption>
<paragraph><location><page_4><loc_50><loc_63><loc_89><loc_68></location>one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples.</paragraph>
<paragraph><location><page_4><loc_52><loc_61><loc_89><loc_62></location>Tab. 1 summarizes the various attributes of the datasets.</paragraph>
<subtitle-level-1><location><page_4><loc_50><loc_58><loc_73><loc_59></location>4. The TableFormer model</subtitle-level-1>
@ -87,12 +84,10 @@
<subtitle-level-1><location><page_4><loc_50><loc_41><loc_69><loc_42></location>4.1. Model architecture.</subtitle-level-1>
<paragraph><location><page_4><loc_50><loc_16><loc_89><loc_40></location>We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification.</paragraph>
<paragraph><location><page_4><loc_50><loc_10><loc_89><loc_16></location>CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per-</paragraph>
<caption><location><page_5><loc_8><loc_72><loc_89><loc_74></location>Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure.</caption>
<figure>
<location><page_5><loc_12><loc_77><loc_85><loc_90></location>
<caption>Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure.</caption>
</figure>
<caption><location><page_5><loc_8><loc_14><loc_47><loc_33></location>Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes.</caption>
<figure>
<location><page_5><loc_9><loc_36><loc_47><loc_67></location>
<caption>Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes.</caption>
@ -110,8 +105,7 @@
<subtitle-level-1><location><page_6><loc_8><loc_28><loc_28><loc_30></location>5. Experimental Results</subtitle-level-1>
<subtitle-level-1><location><page_6><loc_8><loc_26><loc_29><loc_27></location>5.1. Implementation Details</subtitle-level-1>
<paragraph><location><page_6><loc_8><loc_19><loc_47><loc_25></location>TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:</paragraph>
<paragraph><location><page_6><loc_8><loc_10><loc_47><loc_13></location>Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved</paragraph>
<paragraph><location><page_6><loc_50><loc_86><loc_89><loc_91></location>runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions.</paragraph>
<paragraph><location><page_6><loc_8><loc_10><loc_47><loc_13></location><location><page_6><loc_8><loc_10><loc_47><loc_13></location>Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions.</paragraph>
<paragraph><location><page_6><loc_50><loc_59><loc_89><loc_85></location>The Transformer Encoder consists of two "Transformer Encoder Layers", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four "Transformer Decoder Layers" with similar input and output dimensions as the "Transformer Encoder Layers". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5.</paragraph>
<paragraph><location><page_6><loc_50><loc_46><loc_89><loc_58></location>For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and λ set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence.</paragraph>
<paragraph><location><page_6><loc_50><loc_30><loc_89><loc_45></location>TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag.</paragraph>
@ -123,10 +117,8 @@
<paragraph><location><page_7><loc_8><loc_73><loc_47><loc_77></location>where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .</paragraph>
<subtitle-level-1><location><page_7><loc_8><loc_70><loc_28><loc_72></location>5.4. Quantitative Analysis</subtitle-level-1>
<paragraph><location><page_7><loc_8><loc_50><loc_47><loc_69></location>Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size.</paragraph>
<caption><location><page_7><loc_8><loc_23><loc_47><loc_25></location>Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).</caption>
<table>
<location><page_7><loc_9><loc_26><loc_46><loc_48></location>
<caption>Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).</caption>
<row_0><col_0><col_header>Model</col_0><col_1><col_header>Dataset</col_1><col_2><col_header>Simple</col_2><col_3><col_header>TEDS Complex</col_3><col_4><col_header>All</col_4></row_0>
<row_1><col_0><row_header>EDD</col_0><col_1><body>PTN</col_1><col_2><body>91.1</col_2><col_3><body>88.7</col_3><col_4><body>89.9</col_4></row_1>
<row_2><col_0><row_header>GTE</col_0><col_1><body>PTN</col_1><col_2><body>-</col_2><col_3><body>-</col_3><col_4><body>93.01</col_4></row_2>
@ -139,10 +131,9 @@
<row_9><col_0><row_header>TableFormer</col_0><col_1><body>TB</col_1><col_2><body>89.6</col_2><col_3><body>-</col_3><col_4><body>89.6</col_4></row_9>
<row_10><col_0><row_header>TableFormer</col_0><col_1><body>STN</col_1><col_2><body>96.9</col_2><col_3><body>95.7</col_3><col_4><body>96.7</col_4></row_10>
</table>
<paragraph><location><page_7><loc_8><loc_23><loc_47><loc_25></location>Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).</paragraph>
<paragraph><location><page_7><loc_8><loc_21><loc_43><loc_22></location>FT: Model was trained on PubTabNet then finetuned.</paragraph>
<paragraph><location><page_7><loc_8><loc_10><loc_47><loc_19></location>Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate</paragraph>
<paragraph><location><page_7><loc_50><loc_71><loc_89><loc_91></location>our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes.</paragraph>
<caption><location><page_7><loc_50><loc_57><loc_89><loc_60></location>Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.</caption>
<paragraph><location><page_7><loc_8><loc_10><loc_47><loc_19></location><location><page_7><loc_8><loc_10><loc_47><loc_19></location>Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes.</paragraph>
<table>
<location><page_7><loc_50><loc_62><loc_87><loc_69></location>
<caption>Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.</caption>
@ -151,8 +142,8 @@
<row_2><col_0><body>TableFormer</col_0><col_1><body>PubTabNet</col_1><col_2><body>82.1</col_2><col_3><body>86.8</col_3></row_2>
<row_3><col_0><body>TableFormer</col_0><col_1><body>SynthTabNet</col_1><col_2><body>87.7</col_2><col_3><body>-</col_3></row_3>
</table>
<caption><location><page_7><loc_50><loc_57><loc_89><loc_60></location>Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.</caption>
<paragraph><location><page_7><loc_50><loc_34><loc_89><loc_54></location>Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations.</paragraph>
<caption><location><page_7><loc_50><loc_13><loc_89><loc_17></location>Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables.</caption>
<table>
<location><page_7><loc_54><loc_19><loc_85><loc_32></location>
<caption>Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables.</caption>
@ -164,6 +155,7 @@
<row_5><col_0><row_header>EDD</col_0><col_1><body>91.2</col_1><col_2><body>85.4</col_2><col_3><body>88.3</col_3></row_5>
<row_6><col_0><row_header>TableFormer</col_0><col_1><body>95.4</col_1><col_2><body>90.1</col_2><col_3><body>93.6</col_3></row_6>
</table>
<caption><location><page_7><loc_50><loc_13><loc_89><loc_17></location>Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables.</caption>
<paragraph><location><page_8><loc_9><loc_89><loc_10><loc_90></location>- a.</paragraph>
<paragraph><location><page_8><loc_11><loc_89><loc_82><loc_90></location>- Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells</paragraph>
<subtitle-level-1><location><page_8><loc_9><loc_87><loc_46><loc_88></location>Japanese language (previously unseen by TableFormer):</subtitle-level-1>
@ -171,13 +163,13 @@
<figure>
<location><page_8><loc_8><loc_76><loc_49><loc_87></location>
</figure>
<caption><location><page_8><loc_9><loc_73><loc_63><loc_74></location>b. Structure predicted by TableFormer, with superimposed matched PDF cell text:</caption>
<figure>
<location><page_8><loc_50><loc_77><loc_91><loc_88></location>
<caption>b. Structure predicted by TableFormer, with superimposed matched PDF cell text:</caption>
</figure>
<table>
<location><page_8><loc_9><loc_63><loc_49><loc_72></location>
<caption>Text is aligned to match original for ease of viewing</caption>
<row_0><col_0><body></col_0><col_1><body></col_1><col_2><col_header>論文ファイル</col_2><col_3><col_header>論文ファイル</col_3><col_4><col_header>参考文献</col_4><col_5><col_header>参考文献</col_5></row_0>
<row_1><col_0><col_header>出典</col_0><col_1><col_header>ファイル 数</col_1><col_2><col_header>英語</col_2><col_3><col_header>日本語</col_3><col_4><col_header>英語</col_4><col_5><col_header>日本語</col_5></row_1>
<row_2><col_0><row_header>Association for Computational Linguistics(ACL2003)</col_0><col_1><body>65</col_1><col_2><body>65</col_2><col_3><body>0</col_3><col_4><body>150</col_4><col_5><body>0</col_5></row_2>
@ -192,7 +184,6 @@
<caption><location><page_8><loc_62><loc_62><loc_90><loc_63></location>Text is aligned to match original for ease of viewing</caption>
<table>
<location><page_8><loc_50><loc_64><loc_90><loc_72></location>
<caption>Text is aligned to match original for ease of viewing</caption>
<row_0><col_0><body></col_0><col_1><col_header>Shares (in millions)</col_1><col_2><col_header>Shares (in millions)</col_2><col_3><col_header>Weighted Average Grant Date Fair Value</col_3><col_4><col_header>Weighted Average Grant Date Fair Value</col_4></row_0>
<row_1><col_0><body></col_0><col_1><col_header>RS U s</col_1><col_2><col_header>PSUs</col_2><col_3><col_header>RSUs</col_3><col_4><col_header>PSUs</col_4></row_1>
<row_2><col_0><row_header>Nonvested on Janua ry 1</col_0><col_1><body>1. 1</col_1><col_2><body>0.3</col_2><col_3><body>90.10 $</col_3><col_4><body>$ 91.19</col_4></row_2>
@ -201,38 +192,36 @@
<row_5><col_0><row_header>Canceled or forfeited</col_0><col_1><body>(0. 1 )</col_1><col_2><body>-</col_2><col_3><body>102.01</col_3><col_4><body>92.18</col_4></row_5>
<row_6><col_0><row_header>Nonvested on December 31</col_0><col_1><body>1.0</col_1><col_2><body>0.3</col_2><col_3><body>104.85 $</col_3><col_4><body>$ 104.51</col_4></row_6>
</table>
<caption><location><page_8><loc_8><loc_54><loc_89><loc_59></location>Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.</caption>
<figure>
<location><page_8><loc_8><loc_44><loc_35><loc_52></location>
<caption>Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.</caption>
</figure>
<figure>
<location><page_8><loc_35><loc_44><loc_61><loc_52></location>
<caption>Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.</caption>
</figure>
<figure>
<location><page_8><loc_63><loc_44><loc_89><loc_52></location>
</figure>
<caption><location><page_8><loc_10><loc_41><loc_87><loc_42></location>Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.</caption>
<figure>
<location><page_8><loc_35><loc_44><loc_61><loc_52></location>
<caption>Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.</caption>
</figure>
<subtitle-level-1><location><page_8><loc_8><loc_37><loc_27><loc_38></location>5.5. Qualitative Analysis</subtitle-level-1>
<paragraph><location><page_8><loc_8><loc_10><loc_47><loc_32></location>We showcase several visualizations for the different components of our network on various "complex" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type.</paragraph>
<subtitle-level-1><location><page_8><loc_50><loc_37><loc_75><loc_38></location>6. Future Work & Conclusion</subtitle-level-1>
<paragraph><location><page_8><loc_8><loc_10><loc_47><loc_32></location>We showcase several visualizations for the different components of our network on various "complex" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type.</paragraph>
<paragraph><location><page_8><loc_50><loc_18><loc_89><loc_35></location>In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce "SynthTabNet" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets.</paragraph>
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<paragraph><location><page_9><loc_50><loc_53><loc_89><loc_58></location>- [20] Rujiao Long, Wen Wang, Nan Xue, Feiyu Gao, Zhibo Yang, Yongpan Wang, and Gui-Song Xia. Parsing table structures in the wild. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 944-952, 2021. 2</paragraph>
<paragraph><location><page_9><loc_50><loc_45><loc_89><loc_53></location>- [21] Shubham Singh Paliwal, D Vishwanath, Rohit Rahul, Monika Sharma, and Lovekesh Vig. Tablenet: Deep learning model for end-to-end table detection and tabular data extraction from scanned document images. In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 128-133. IEEE, 2019. 1</paragraph>
<paragraph><location><page_9><loc_50><loc_30><loc_89><loc_44></location>- [22] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch'e-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32 , pages 8024-8035. Curran Associates, Inc., 2019. 6</paragraph>
<paragraph><location><page_9><loc_50><loc_30><loc_89><loc_44></location>- [22] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch´e-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32 , pages 8024-8035. Curran Associates, Inc., 2019. 6</paragraph>
<paragraph><location><page_9><loc_50><loc_21><loc_89><loc_29></location>- [23] Devashish Prasad, Ayan Gadpal, Kshitij Kapadni, Manish Visave, and Kavita Sultanpure. Cascadetabnet: An approach for end to end table detection and structure recognition from image-based documents. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , pages 572-573, 2020. 1</paragraph>
<paragraph><location><page_9><loc_50><loc_16><loc_89><loc_21></location>- [24] Shah Rukh Qasim, Hassan Mahmood, and Faisal Shafait. Rethinking table recognition using graph neural networks. In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 142-147. IEEE, 2019. 3</paragraph>
<paragraph><location><page_9><loc_50><loc_10><loc_89><loc_15></location>- [25] Hamid Rezatofighi, Nathan Tsoi, JunYoung Gwak, Amir Sadeghian, Ian Reid, and Silvio Savarese. Generalized intersection over union: A metric and a loss for bounding box regression. In Proceedings of the IEEE/CVF Conference on</paragraph>
@ -267,8 +256,7 @@
<paragraph><location><page_11><loc_8><loc_21><loc_47><loc_51></location>We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes.</paragraph>
<paragraph><location><page_11><loc_8><loc_18><loc_47><loc_20></location>Figure 7 illustrates the distribution of the tables across different dimensions per dataset.</paragraph>
<subtitle-level-1><location><page_11><loc_8><loc_15><loc_25><loc_16></location>1.2. Synthetic datasets</subtitle-level-1>
<paragraph><location><page_11><loc_8><loc_10><loc_47><loc_14></location>Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-</paragraph>
<paragraph><location><page_11><loc_50><loc_74><loc_89><loc_79></location>ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%).</paragraph>
<paragraph><location><page_11><loc_8><loc_10><loc_47><loc_14></location><location><page_11><loc_8><loc_10><loc_47><loc_14></location>Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear- ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%).</paragraph>
<paragraph><location><page_11><loc_50><loc_71><loc_89><loc_73></location>The process of generating a synthetic dataset can be decomposed into the following steps:</paragraph>
<paragraph><location><page_11><loc_50><loc_60><loc_89><loc_70></location>- 1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.).</paragraph>
<paragraph><location><page_11><loc_50><loc_43><loc_89><loc_60></location>- 2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans.</paragraph>
@ -277,13 +265,13 @@
<paragraph><location><page_11><loc_50><loc_23><loc_89><loc_31></location>- 5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process.</paragraph>
<subtitle-level-1><location><page_11><loc_50><loc_18><loc_89><loc_21></location>2. Prediction post-processing for PDF documents</subtitle-level-1>
<paragraph><location><page_11><loc_50><loc_10><loc_89><loc_17></location>Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons:</paragraph>
<caption><location><page_12><loc_8><loc_76><loc_89><loc_79></location>Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.</caption>
<figure>
<location><page_12><loc_9><loc_81><loc_89><loc_91></location>
<caption>Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity.</caption>
</figure>
<paragraph><location><page_12><loc_10><loc_71><loc_47><loc_73></location>- · TableFormer output does not include the table cell content.</paragraph>
<paragraph><location><page_12><loc_10><loc_67><loc_47><loc_69></location>- · There are occasional inaccuracies in the predictions of the bounding boxes.</paragraph>
<paragraph><location><page_12><loc_50><loc_68><loc_89><loc_73></location>dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal.</paragraph>
<paragraph><location><page_12><loc_8><loc_50><loc_47><loc_65></location>However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes.</paragraph>
<paragraph><location><page_12><loc_8><loc_47><loc_47><loc_50></location>Here is a step-by-step description of the prediction postprocessing:</paragraph>
<paragraph><location><page_12><loc_8><loc_42><loc_47><loc_47></location>- 1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.</paragraph>
@ -293,7 +281,6 @@
<paragraph><location><page_12><loc_8><loc_24><loc_47><loc_28></location>- 4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:</paragraph>
<paragraph><location><page_12><loc_8><loc_13><loc_47><loc_16></location>where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point.</paragraph>
<paragraph><location><page_12><loc_8><loc_10><loc_47><loc_13></location>- 5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-</paragraph>
<paragraph><location><page_12><loc_50><loc_68><loc_89><loc_73></location>dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal.</paragraph>
<paragraph><location><page_12><loc_50><loc_65><loc_89><loc_67></location>- 6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.</paragraph>
<paragraph><location><page_12><loc_50><loc_51><loc_89><loc_64></location>- 7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.</paragraph>
<paragraph><location><page_12><loc_50><loc_42><loc_89><loc_51></location>- 8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score.</paragraph>
@ -315,14 +302,15 @@
<table>
<location><page_13><loc_14><loc_54><loc_39><loc_61></location>
</table>
<caption><location><page_13><loc_10><loc_35><loc_45><loc_37></location>Figure 8: Example of a table with multi-line header.</caption>
<table>
<location><page_13><loc_14><loc_38><loc_41><loc_50></location>
<caption>Figure 8: Example of a table with multi-line header.</caption>
</table>
<caption><location><page_13><loc_10><loc_35><loc_45><loc_37></location>Figure 8: Example of a table with multi-line header.</caption>
<table>
<location><page_13><loc_51><loc_83><loc_91><loc_87></location>
<caption>Figure 9: Example of a table with big empty distance between cells.</caption>
</table>
<caption><location><page_13><loc_50><loc_59><loc_89><loc_61></location>Figure 9: Example of a table with big empty distance between cells.</caption>
<table>
<location><page_13><loc_51><loc_77><loc_91><loc_80></location>
</table>
@ -332,14 +320,14 @@
<figure>
<location><page_13><loc_51><loc_63><loc_70><loc_68></location>
</figure>
<caption><location><page_13><loc_50><loc_59><loc_89><loc_61></location>Figure 9: Example of a table with big empty distance between cells.</caption>
<table>
<location><page_13><loc_51><loc_63><loc_70><loc_68></location>
<caption>Figure 9: Example of a table with big empty distance between cells.</caption>
</table>
<table>
<location><page_13><loc_55><loc_45><loc_80><loc_51></location>
<caption>Figure 10: Example of a complex table with empty cells.</caption>
</table>
<caption><location><page_13><loc_51><loc_13><loc_89><loc_14></location>Figure 10: Example of a complex table with empty cells.</caption>
<table>
<location><page_13><loc_55><loc_37><loc_80><loc_43></location>
</table>
@ -349,19 +337,16 @@
<figure>
<location><page_13><loc_55><loc_16><loc_85><loc_25></location>
</figure>
<caption><location><page_13><loc_51><loc_13><loc_89><loc_14></location>Figure 10: Example of a complex table with empty cells.</caption>
<table>
<location><page_13><loc_55><loc_16><loc_85><loc_25></location>
<caption>Figure 10: Example of a complex table with empty cells.</caption>
</table>
<table>
<location><page_14><loc_8><loc_57><loc_46><loc_65></location>
</table>
<caption><location><page_14><loc_8><loc_52><loc_47><loc_55></location>Figure 11: Simple table with different style and empty cells.</caption>
<figure>
<location><page_14><loc_8><loc_56><loc_46><loc_87></location>
<caption>Figure 11: Simple table with different style and empty cells.</caption>
</figure>
<caption><location><page_14><loc_8><loc_52><loc_47><loc_55></location>Figure 11: Simple table with different style and empty cells.</caption>
<table>
<location><page_14><loc_8><loc_38><loc_51><loc_43></location>
</table>
@ -371,11 +356,10 @@
<table>
<location><page_14><loc_8><loc_25><loc_51><loc_30></location>
</table>
<caption><location><page_14><loc_9><loc_14><loc_46><loc_15></location>Figure 12: Simple table predictions and post processing.</caption>
<figure>
<location><page_14><loc_8><loc_17><loc_29><loc_23></location>
<caption>Figure 12: Simple table predictions and post processing.</caption>
</figure>
<caption><location><page_14><loc_9><loc_14><loc_46><loc_15></location>Figure 12: Simple table predictions and post processing.</caption>
<table>
<location><page_14><loc_52><loc_73><loc_87><loc_80></location>
</table>
@ -385,24 +369,23 @@
<table>
<location><page_14><loc_54><loc_55><loc_86><loc_64></location>
</table>
<caption><location><page_14><loc_52><loc_52><loc_88><loc_53></location>Figure 13: Table predictions example on colorful table.</caption>
<figure>
<location><page_14><loc_52><loc_55><loc_87><loc_89></location>
<caption>Figure 13: Table predictions example on colorful table.</caption>
</figure>
<table>
<location><page_14><loc_52><loc_40><loc_85><loc_46></location>
<caption>Figure 14: Example with multi-line text.</caption>
</table>
<caption><location><page_14><loc_56><loc_13><loc_83><loc_14></location>Figure 14: Example with multi-line text.</caption>
<table>
<location><page_14><loc_52><loc_32><loc_85><loc_38></location>
</table>
<table>
<location><page_14><loc_52><loc_25><loc_85><loc_31></location>
</table>
<caption><location><page_14><loc_56><loc_13><loc_83><loc_14></location>Figure 14: Example with multi-line text.</caption>
<table>
<location><page_14><loc_52><loc_16><loc_87><loc_23></location>
<caption>Figure 14: Example with multi-line text.</caption>
</table>
<figure>
<location><page_15><loc_9><loc_69><loc_46><loc_83></location>
@ -422,14 +405,11 @@
<figure>
<location><page_15><loc_8><loc_20><loc_52><loc_36></location>
</figure>
<caption><location><page_15><loc_14><loc_18><loc_41><loc_19></location>Figure 15: Example with triangular table.</caption>
<table>
<location><page_15><loc_8><loc_20><loc_52><loc_36></location>
<caption>Figure 15: Example with triangular table.</caption>
</table>
<table>
<location><page_15><loc_53><loc_72><loc_86><loc_85></location>
<caption>Figure 15: Example with triangular table.</caption>
</table>
<caption><location><page_15><loc_14><loc_18><loc_41><loc_19></location>Figure 15: Example with triangular table.</caption>
<table>
<location><page_15><loc_53><loc_57><loc_86><loc_69></location>
</table>
@ -442,12 +422,13 @@
<figure>
<location><page_15><loc_58><loc_20><loc_81><loc_38></location>
</figure>
<caption><location><page_15><loc_50><loc_15><loc_89><loc_18></location>Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact.</caption>
<table>
<location><page_15><loc_58><loc_20><loc_81><loc_38></location>
<caption>Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact.</caption>
</table>
<caption><location><page_16><loc_8><loc_33><loc_89><loc_36></location>Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure.</caption>
<table>
<location><page_15><loc_8><loc_20><loc_52><loc_36></location>
</table>
<caption><location><page_15><loc_50><loc_15><loc_89><loc_18></location>Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact.</caption>
<figure>
<location><page_16><loc_11><loc_37><loc_86><loc_68></location>
<caption>Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure.</caption>

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## a. Picture of a table:
Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.
## 1. Introduction
The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues.
<!-- image -->
Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables.
- b. Red-annotation of bounding boxes, Blue-predictions by TableFormer
<!-- image -->
- c. Structure predicted by TableFormer:
<!-- image -->
Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'.
<!-- image -->
| 0 | 1 | 1 | 2 1 | 2 1 | |
|-----|-----|-----|-------|-------|----|
@ -155,9 +152,7 @@ where λ ∈ [0, 1], and λ$_{iou}$, λ$_{l}$$_{1}$ ∈$_{R}$ are hyper-paramete
TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints:
Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved
runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions.
Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions.
The Transformer Encoder consists of two "Transformer Encoder Layers", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four "Transformer Decoder Layers" with similar input and output dimensions as the "Transformer Encoder Layers". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5.
@ -181,8 +176,6 @@ where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDi
Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size.
Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).
| Model | Dataset | Simple | TEDS Complex | All |
|-------------|-----------|----------|----------------|-------|
| EDD | PTN | 91.1 | 88.7 | 89.9 |
@ -196,11 +189,11 @@ Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) a
| TableFormer | TB | 89.6 | - | 89.6 |
| TableFormer | STN | 96.9 | 95.7 | 96.7 |
Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN).
FT: Model was trained on PubTabNet then finetuned.
Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate
our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes.
Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes.
Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing.
@ -236,6 +229,8 @@ Table 4: Results of structure with content retrieved using cell detection on Pub
b. Structure predicted by TableFormer, with superimposed matched PDF cell text:
<!-- image -->
Text is aligned to match original for ease of viewing
| | | 論文ファイル | 論文ファイル | 参考文献 | 参考文献 |
|----------------------------------------------------|-------------|----------------|----------------|------------|------------|
| 出典 | ファイル 数 | 英語 | 日本語 | 英語 | 日本語 |
@ -248,8 +243,6 @@ b. Structure predicted by TableFormer, with superimposed matched PDF cell text:
| WWW から収集した論文 | 107 | 73 | 34 | 147 | 96 |
| | 945 | 294 | 651 | 1122 | 955 |
Text is aligned to match original for ease of viewing
| | Shares (in millions) | Shares (in millions) | Weighted Average Grant Date Fair Value | Weighted Average Grant Date Fair Value |
|--------------------------|------------------------|------------------------|------------------------------------------|------------------------------------------|
| | RS U s | PSUs | RSUs | PSUs |
@ -259,20 +252,20 @@ Text is aligned to match original for ease of viewing
| Canceled or forfeited | (0. 1 ) | - | 102.01 | 92.18 |
| Nonvested on December 31 | 1.0 | 0.3 | 104.85 $ | $ 104.51 |
Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.
<!-- image -->
Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset.
<!-- image -->
<!-- image -->
Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table.
<!-- image -->
## 5.5. Qualitative Analysis
We showcase several visualizations for the different components of our network on various "complex" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type.
## 6. Future Work & Conclusion
We showcase several visualizations for the different components of our network on various "complex" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type.
In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce "SynthTabNet" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets.
## References
@ -285,11 +278,11 @@ In this paper, we presented TableFormer an end-to-end transformer based approach
- [3] Bertrand Couasnon and Aurelie Lemaitre. Recognition of Tables and Forms , pages 647-677. Springer London, London, 2014. 2
- [4] Herv'e D'ejean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and Recognition (cTDaR), Apr. 2019. http://sac.founderit.com/. 2
- [4] Herv´e D´ejean, Jean-Luc Meunier, Liangcai Gao, Yilun Huang, Yu Fang, Florian Kleber, and Eva-Maria Lang. ICDAR 2019 Competition on Table Detection and Recognition (cTDaR), Apr. 2019. http://sac.founderit.com/. 2
- [5] Basilios Gatos, Dimitrios Danatsas, Ioannis Pratikakis, and Stavros J Perantonis. Automatic table detection in document images. In International Conference on Pattern Recognition and Image Analysis , pages 609-618. Springer, 2005. 2
- [6] Max Gobel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. In 2013 12th International Conference on Document Analysis and Recognition , pages 1449-1453, 2013. 2
- [6] Max G¨obel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. In 2013 12th International Conference on Document Analysis and Recognition , pages 1449-1453, 2013. 2
- [7] EA Green and M Krishnamoorthy. Recognition of tables using table grammars. procs. In Symposium on Document Analysis and Recognition (SDAIR'95) , pages 261-277. 2
@ -303,7 +296,7 @@ In this paper, we presented TableFormer an end-to-end transformer based approach
- [12] Matthew Hurst. A constraint-based approach to table structure derivation. In Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2 , ICDAR '03, page 911, USA, 2003. IEEE Computer Society. 2
- [13] Thotreingam Kasar, Philippine Barlas, Sebastien Adam, Cl'ement Chatelain, and Thierry Paquet. Learning to detect tables in scanned document images using line information. In 2013 12th International Conference on Document Analysis and Recognition , pages 1185-1189. IEEE, 2013. 2
- [13] Thotreingam Kasar, Philippine Barlas, Sebastien Adam, Cl´ement Chatelain, and Thierry Paquet. Learning to detect tables in scanned document images using line information. In 2013 12th International Conference on Document Analysis and Recognition , pages 1185-1189. IEEE, 2013. 2
- [14] Pratik Kayal, Mrinal Anand, Harsh Desai, and Mayank Singh. Icdar 2021 competition on scientific table image recognition to latex, 2021. 2
@ -321,7 +314,7 @@ In this paper, we presented TableFormer an end-to-end transformer based approach
- [21] Shubham Singh Paliwal, D Vishwanath, Rohit Rahul, Monika Sharma, and Lovekesh Vig. Tablenet: Deep learning model for end-to-end table detection and tabular data extraction from scanned document images. In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 128-133. IEEE, 2019. 1
- [22] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch'e-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32 , pages 8024-8035. Curran Associates, Inc., 2019. 6
- [22] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alch´e-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32 , pages 8024-8035. Curran Associates, Inc., 2019. 6
- [23] Devashish Prasad, Ayan Gadpal, Kshitij Kapadni, Manish Visave, and Kavita Sultanpure. Cascadetabnet: An approach for end to end table detection and structure recognition from image-based documents. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , pages 572-573, 2020. 1
@ -373,9 +366,7 @@ Figure 7 illustrates the distribution of the tables across different dimensions
## 1.2. Synthetic datasets
Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear-
ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%).
Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear- ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%).
The process of generating a synthetic dataset can be decomposed into the following steps:
@ -400,6 +391,8 @@ Figure 7: Distribution of the tables across different dimensions per dataset. Si
- · There are occasional inaccuracies in the predictions of the bounding boxes.
dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal.
However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes.
Here is a step-by-step description of the prediction postprocessing:
@ -418,8 +411,6 @@ where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for t
- 5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-
dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal.
- 6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.
- 7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells.
@ -446,10 +437,6 @@ Aditional images with examples of TableFormer predictions and post-processing ca
Figure 8: Example of a table with multi-line header.
<!-- image -->
Figure 9: Example of a table with big empty distance between cells.
@ -460,11 +447,15 @@ Figure 10: Example of a complex table with empty cells.
<!-- image -->
<!-- image -->
Figure 11: Simple table with different style and empty cells.
<!-- image -->
Figure 12: Simple table predictions and post processing.
<!-- image -->
Figure 13: Table predictions example on colorful table.
<!-- image -->
@ -491,7 +482,5 @@ Figure 15: Example with triangular table.
Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact.
Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure.
<!-- image -->

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@ -10,11 +10,10 @@
<subtitle-level-1><location><page_1><loc_9><loc_29><loc_22><loc_30></location>CCS CONCEPTS</subtitle-level-1>
<paragraph><location><page_1><loc_9><loc_25><loc_49><loc_29></location>· Information systems → Document structure ; · Applied computing → Document analysis ; · Computing methodologies → Machine learning ; Computer vision ; Object detection ;</paragraph>
<paragraph><location><page_1><loc_9><loc_15><loc_48><loc_20></location>Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).</paragraph>
<paragraph><location><page_1><loc_9><loc_14><loc_32><loc_15></location>KDD '22, August 14-18, 2022, Washington, DC, USA</paragraph>
<paragraph><location><page_1><loc_9><loc_14><loc_32><loc_15></location>KDD ’22, August 14-18, 2022, Washington, DC, USA</paragraph>
<paragraph><location><page_1><loc_9><loc_13><loc_31><loc_14></location>© 2022 Copyright held by the owner/author(s).</paragraph>
<paragraph><location><page_1><loc_9><loc_12><loc_26><loc_13></location>ACM ISBN 978-1-4503-9385-0/22/08.</paragraph>
<paragraph><location><page_1><loc_9><loc_11><loc_27><loc_12></location>https://doi.org/10.1145/3534678.3539043</paragraph>
<caption><location><page_1><loc_52><loc_29><loc_91><loc_32></location>Figure 1: Four examples of complex page layouts across different document categories</caption>
<figure>
<location><page_1><loc_53><loc_34><loc_90><loc_68></location>
<caption>Figure 1: Four examples of complex page layouts across different document categories</caption>
@ -41,7 +40,6 @@
<subtitle-level-1><location><page_2><loc_52><loc_27><loc_78><loc_29></location>3 THE DOCLAYNET DATASET</subtitle-level-1>
<paragraph><location><page_2><loc_52><loc_15><loc_91><loc_25></location>DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4.</paragraph>
<paragraph><location><page_2><loc_52><loc_11><loc_91><loc_14></location>In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents</paragraph>
<caption><location><page_3><loc_9><loc_68><loc_48><loc_70></location>Figure 2: Distribution of DocLayNet pages across document categories.</caption>
<figure>
<location><page_3><loc_14><loc_72><loc_43><loc_88></location>
<caption>Figure 2: Distribution of DocLayNet pages across document categories.</caption>
@ -55,7 +53,6 @@
<paragraph><location><page_3><loc_52><loc_26><loc_91><loc_65></location>Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, "invisible" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as "invisible" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a "natural" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4.</paragraph>
<subtitle-level-1><location><page_3><loc_52><loc_22><loc_77><loc_23></location>4 ANNOTATION CAMPAIGN</subtitle-level-1>
<paragraph><location><page_3><loc_52><loc_11><loc_91><loc_20></location>The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four,</paragraph>
<caption><location><page_4><loc_9><loc_85><loc_91><loc_89></location>Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row "Total") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.</caption>
<table>
<location><page_4><loc_16><loc_63><loc_84><loc_83></location>
<caption>Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row "Total") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.</caption>
@ -74,14 +71,13 @@
<row_12><col_0><row_header>Title</col_0><col_1><body>5071</col_1><col_2><body>0.47</col_2><col_3><body>0.30</col_3><col_4><body>0.50</col_4><col_5><body>60-72</col_5><col_6><body>24-63</col_6><col_7><body>50-63</col_7><col_8><body>94-100</col_8><col_9><body>82-96</col_9><col_10><body>68-79</col_10><col_11><body>24-56</col_11></row_12>
<row_13><col_0><row_header>Total</col_0><col_1><body>1107470</col_1><col_2><body>941123</col_2><col_3><body>99816</col_3><col_4><body>66531</col_4><col_5><body>82-83</col_5><col_6><body>71-74</col_6><col_7><body>79-81</col_7><col_8><body>89-94</col_8><col_9><body>86-91</col_9><col_10><body>71-76</col_10><col_11><body>68-85</col_11></row_13>
</table>
<caption><location><page_4><loc_9><loc_23><loc_48><loc_30></location>Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.</caption>
<caption><location><page_4><loc_9><loc_85><loc_91><loc_89></location>Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row "Total") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges.</caption>
<figure>
<location><page_4><loc_9><loc_32><loc_48><loc_61></location>
<caption>Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right.</caption>
</figure>
<paragraph><location><page_4><loc_9><loc_15><loc_48><loc_20></location>we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.</paragraph>
<paragraph><location><page_4><loc_9><loc_11><loc_48><loc_14></location>Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources</paragraph>
<paragraph><location><page_4><loc_52><loc_53><loc_91><loc_61></location>include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.</paragraph>
<paragraph><location><page_4><loc_9><loc_11><loc_48><loc_14></location><location><page_4><loc_9><loc_11><loc_48><loc_14></location>Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.</paragraph>
<paragraph><location><page_4><loc_52><loc_36><loc_91><loc_52></location>Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.</paragraph>
<paragraph><location><page_4><loc_52><loc_12><loc_91><loc_36></location>Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on</paragraph>
<paragraph><location><page_5><loc_9><loc_87><loc_48><loc_89></location>the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category.</paragraph>
@ -95,18 +91,16 @@
<paragraph><location><page_5><loc_11><loc_34><loc_48><loc_38></location>- (6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line.</paragraph>
<paragraph><location><page_5><loc_9><loc_27><loc_48><loc_33></location>The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference.</paragraph>
<paragraph><location><page_5><loc_9><loc_11><loc_48><loc_27></location>Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations</paragraph>
<caption><location><page_5><loc_52><loc_36><loc_91><loc_40></location>Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.</caption>
<figure>
<location><page_5><loc_52><loc_42><loc_91><loc_89></location>
<caption>Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.</caption>
</figure>
<paragraph><location><page_5><loc_65><loc_42><loc_78><loc_42></location>05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0</paragraph>
<caption><location><page_5><loc_52><loc_36><loc_91><loc_40></location>Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.</caption>
<paragraph><location><page_5><loc_52><loc_31><loc_91><loc_34></location>were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar.</paragraph>
<paragraph><location><page_5><loc_52><loc_10><loc_91><loc_31></location>Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted</paragraph>
<caption><location><page_6><loc_9><loc_77><loc_48><loc_89></location>Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.</caption>
<paragraph><location><page_6><loc_9><loc_77><loc_48><loc_89></location>Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.</paragraph>
<table>
<location><page_6><loc_10><loc_56><loc_47><loc_75></location>
<caption>Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset.</caption>
<row_0><col_0><body></col_0><col_1><col_header>human</col_1><col_2><col_header>MRCNN</col_2><col_3><col_header>MRCNN</col_3><col_4><col_header>FRCNN</col_4><col_5><col_header>YOLO</col_5></row_0>
<row_1><col_0><body></col_0><col_1><col_header>human</col_1><col_2><col_header>R50</col_2><col_3><col_header>R101</col_3><col_4><col_header>R101</col_4><col_5><col_header>v5x6</col_5></row_1>
<row_2><col_0><row_header>Caption</col_0><col_1><body>84-89</col_1><col_2><body>68.4</col_2><col_3><body>71.5</col_3><col_4><body>70.1</col_4><col_5><body>77.7</col_5></row_2>
@ -125,7 +119,6 @@
<paragraph><location><page_6><loc_9><loc_27><loc_48><loc_53></location>to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity.</paragraph>
<subtitle-level-1><location><page_6><loc_9><loc_24><loc_24><loc_26></location>5 EXPERIMENTS</subtitle-level-1>
<paragraph><location><page_6><loc_9><loc_10><loc_48><loc_23></location>The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this</paragraph>
<caption><location><page_6><loc_52><loc_57><loc_91><loc_65></location>Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.</caption>
<figure>
<location><page_6><loc_53><loc_67><loc_90><loc_89></location>
<caption>Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions.</caption>
@ -135,10 +128,8 @@
<subtitle-level-1><location><page_6><loc_52><loc_36><loc_76><loc_37></location>Baselines for Object Detection</subtitle-level-1>
<paragraph><location><page_6><loc_52><loc_11><loc_91><loc_35></location>In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 × 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document.</paragraph>
<paragraph><location><page_7><loc_9><loc_84><loc_48><loc_89></location>Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels.</paragraph>
<caption><location><page_7><loc_52><loc_84><loc_91><loc_89></location>Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement.</caption>
<table>
<location><page_7><loc_13><loc_63><loc_44><loc_81></location>
<caption>Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement.</caption>
<row_0><col_0><col_header>Class-count</col_0><col_1><col_header>11</col_1><col_2><col_header>6</col_2><col_3><col_header>5</col_3><col_4><col_header>4</col_4></row_0>
<row_1><col_0><row_header>Caption</col_0><col_1><body>68</col_1><col_2><body>Text</col_2><col_3><body>Text</col_3><col_4><body>Text</col_4></row_1>
<row_2><col_0><row_header>Footnote</col_0><col_1><body>71</col_1><col_2><body>Text</col_2><col_3><body>Text</col_3><col_4><body>Text</col_4></row_2>
@ -157,6 +148,7 @@
<paragraph><location><page_7><loc_9><loc_33><loc_48><loc_58></location>One of the fundamental questions related to any dataset is if it is "large enough". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles.</paragraph>
<subtitle-level-1><location><page_7><loc_9><loc_30><loc_27><loc_32></location>Impact of Class Labels</subtitle-level-1>
<paragraph><location><page_7><loc_9><loc_11><loc_48><loc_30></location>The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption → Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of</paragraph>
<paragraph><location><page_7><loc_52><loc_84><loc_91><loc_89></location>Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement.</paragraph>
<table>
<location><page_7><loc_58><loc_61><loc_85><loc_81></location>
<row_0><col_0><body>Class-count</col_0><col_1><col_header>11</col_1><col_2><col_header>11</col_2><col_3><col_header>5</col_3><col_4><col_header>5</col_4></row_0>
@ -179,10 +171,9 @@
<paragraph><location><page_7><loc_52><loc_25><loc_91><loc_44></location>Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains ˜ 10% in mAP over the document-wise splitting. Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided.</paragraph>
<subtitle-level-1><location><page_7><loc_52><loc_22><loc_68><loc_23></location>Dataset Comparison</subtitle-level-1>
<paragraph><location><page_7><loc_52><loc_11><loc_91><loc_21></location>Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture ,</paragraph>
<caption><location><page_8><loc_9><loc_81><loc_48><loc_89></location>Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.</caption>
<paragraph><location><page_8><loc_9><loc_81><loc_48><loc_89></location>Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.</paragraph>
<table>
<location><page_8><loc_12><loc_57><loc_45><loc_78></location>
<caption>Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets.</caption>
<row_0><col_0><body></col_0><col_1><body></col_1><col_2><col_header>Testing on</col_2><col_3><col_header>Testing on</col_3><col_4><col_header>Testing on</col_4></row_0>
<row_1><col_0><col_header>Training on</col_0><col_1><col_header>labels</col_1><col_2><col_header>PLN</col_2><col_3><col_header>DB</col_3><col_4><col_header>DLN</col_4></row_1>
<row_2><col_0><row_header>PubLayNet (PLN)</col_0><col_1><row_header>Figure</col_1><col_2><body>96</col_2><col_3><body>43</col_3><col_4><body>23</col_4></row_2>
@ -221,20 +212,19 @@
<paragraph><location><page_8><loc_52><loc_18><loc_91><loc_21></location>- [11] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence , 39(6):1137-1149, 2017.</paragraph>
<paragraph><location><page_8><loc_52><loc_15><loc_91><loc_18></location>- [12] Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick. Mask R-CNN. In IEEE International Conference on Computer Vision , ICCV, pages 2980-2988. IEEE Computer Society, Oct 2017.</paragraph>
<paragraph><location><page_8><loc_52><loc_11><loc_91><loc_15></location>- [13] Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012, TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V, Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy, Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu</paragraph>
<caption><location><page_9><loc_10><loc_43><loc_52><loc_44></location>Text Caption List-Item Formula Table Section-Header Picture Page-Header Page-Footer Title</caption>
<figure>
<location><page_9><loc_9><loc_44><loc_91><loc_89></location>
<caption>Text Caption List-Item Formula Table Section-Header Picture Page-Header Page-Footer Title</caption>
</figure>
<paragraph><location><page_9><loc_9><loc_36><loc_91><loc_41></location>Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion and missing boxes.</paragraph>
<paragraph><location><page_9><loc_11><loc_31><loc_48><loc_33></location>Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ultralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow export, opencv dnn support, October 2021.</paragraph>
<paragraph><location><page_9><loc_52><loc_32><loc_91><loc_33></location>- [20] Shoubin Li, Xuyan Ma, Shuaiqun Pan, Jun Hu, Lin Shi, and Qing Wang. Vtlayout: Fusion of visual and text features for document layout analysis, 2021.</paragraph>
<paragraph><location><page_9><loc_9><loc_28><loc_48><loc_30></location>- [14] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. CoRR , abs/2005.12872, 2020.</paragraph>
<paragraph><location><page_9><loc_9><loc_26><loc_48><loc_27></location>- [15] Mingxing Tan, Ruoming Pang, and Quoc V. Le. Efficientdet: Scalable and efficient object detection. CoRR , abs/1911.09070, 2019.</paragraph>
<paragraph><location><page_9><loc_9><loc_23><loc_48><loc_25></location>- [16] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. Microsoft COCO: common objects in context, 2014.</paragraph>
<paragraph><location><page_9><loc_9><loc_21><loc_48><loc_22></location>- [17] Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick. Detectron2, 2019.</paragraph>
<paragraph><location><page_9><loc_9><loc_16><loc_48><loc_20></location>- [18] Nikolaos Livathinos, Cesar Berrospi, Maksym Lysak, Viktor Kuropiatnyk, Ahmed Nassar, Andre Carvalho, Michele Dolfi, Christoph Auer, Kasper Dinkla, and Peter W. J. Staar. Robust pdf document conversion using recurrent neural networks. In Proceedings of the 35th Conference on Artificial Intelligence , AAAI, pages 1513715145, feb 2021.</paragraph>
<paragraph><location><page_9><loc_9><loc_10><loc_48><loc_15></location>- [19] Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 1192-1200, New York, USA, 2020. Association for Computing Machinery.</paragraph>
<paragraph><location><page_9><loc_52><loc_32><loc_91><loc_33></location>- [20] Shoubin Li, Xuyan Ma, Shuaiqun Pan, Jun Hu, Lin Shi, and Qing Wang. Vtlayout: Fusion of visual and text features for document layout analysis, 2021.</paragraph>
<paragraph><location><page_9><loc_52><loc_29><loc_91><loc_31></location>- [21] Peng Zhang, Can Li, Liang Qiao, Zhanzhan Cheng, Shiliang Pu, Yi Niu, and Fei Wu. Vsr: A unified framework for document layout analysis combining vision, semantics and relations, 2021.</paragraph>
<paragraph><location><page_9><loc_52><loc_25><loc_91><loc_28></location>- [22] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 774-782. ACM, 2018.</paragraph>
<paragraph><location><page_9><loc_52><loc_23><loc_91><loc_24></location>- [23] Connor Shorten and Taghi M. Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of Big Data , 6(1):60, 2019.</paragraph>

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@ -20,7 +20,7 @@ Accurate document layout analysis is a key requirement for highquality PDF docum
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).
KDD '22, August 14-18, 2022, Washington, DC, USA
KDD ’22, August 14-18, 2022, Washington, DC, USA
© 2022 Copyright held by the owner/author(s).
@ -119,9 +119,7 @@ Figure 3: Corpus Conversion Service annotation user interface. The PDF page is s
we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised.
Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources
include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.
Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources include publication repositories such as arXiv$^{3}$, government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process.
Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains.
@ -149,11 +147,12 @@ The complete annotation guideline is over 100 pages long and a detailed descript
Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations
Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.
<!-- image -->
05237a14f2524e3f53c8454b074409d05078038a6a36b770fcc8ec7e540deae0
Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous.
were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar.
Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted
@ -195,8 +194,6 @@ In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], F
Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels.
Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement.
| Class-count | 11 | 6 | 5 | 4 |
|----------------|------|---------|---------|---------|
| Caption | 68 | Text | Text | Text |
@ -220,6 +217,8 @@ One of the fundamental questions related to any dataset is if it is "large enoug
The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption → Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of
Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in GLYPH<tildelow> 10% point improvement.
| Class-count | 11 | 11 | 5 | 5 |
|----------------|------|------|-----|------|
| Split | Doc | Page | Doc | Page |
@ -316,6 +315,8 @@ Figure 6: Example layout predictions on selected pages from the DocLayNet test-s
Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ultralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow export, opencv dnn support, October 2021.
- [20] Shoubin Li, Xuyan Ma, Shuaiqun Pan, Jun Hu, Lin Shi, and Qing Wang. Vtlayout: Fusion of visual and text features for document layout analysis, 2021.
- [14] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. CoRR , abs/2005.12872, 2020.
- [15] Mingxing Tan, Ruoming Pang, and Quoc V. Le. Efficientdet: Scalable and efficient object detection. CoRR , abs/1911.09070, 2019.
@ -328,8 +329,6 @@ Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ultraly
- [19] Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 1192-1200, New York, USA, 2020. Association for Computing Machinery.
- [20] Shoubin Li, Xuyan Ma, Shuaiqun Pan, Jun Hu, Lin Shi, and Qing Wang. Vtlayout: Fusion of visual and text features for document layout analysis, 2021.
- [21] Peng Zhang, Can Li, Liang Qiao, Zhanzhan Cheng, Shiliang Pu, Yi Niu, and Fei Wu. Vsr: A unified framework for document layout analysis combining vision, semantics and relations, 2021.
- [22] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 774-782. ACM, 2018.

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<paragraph><location><page_1><loc_22><loc_81><loc_79><loc_85></location>order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz.</paragraph>
<subtitle-level-1><location><page_1><loc_22><loc_77><loc_52><loc_79></location>5.1 Hyper Parameter Optimization</subtitle-level-1>
<paragraph><location><page_1><loc_22><loc_68><loc_79><loc_77></location>We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML.</paragraph>
<caption><location><page_1><loc_22><loc_59><loc_79><loc_66></location>Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.</caption>
<table>
<location><page_1><loc_23><loc_41><loc_78><loc_57></location>
<caption>Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.</caption>
@ -14,6 +13,7 @@
<row_5><col_0><body>2</col_0><col_1><body>4</col_1><col_2><body>HTML</col_2><col_3><body>0.945</col_3><col_4><body>0.897 0.901</col_4><col_5><body>0.915 0.931</col_5><col_6><body>0.859 0.834</col_6><col_7><body>1.91 3.81</col_7></row_5>
<row_6><col_0><body>4</col_0><col_1><body>2</col_1><col_2><body>OTSL HTML</col_2><col_3><body>0.952 0.944</col_3><col_4><body>0.92 0.903</col_4><col_5><body>0.942 0.931</col_5><col_6><body>0.857 0.824</col_6><col_7><body>1.22 2</col_7></row_6>
</table>
<caption><location><page_1><loc_22><loc_59><loc_79><loc_66></location>Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.</caption>
<subtitle-level-1><location><page_1><loc_22><loc_35><loc_43><loc_36></location>5.2 Quantitative Results</subtitle-level-1>
<paragraph><location><page_1><loc_22><loc_22><loc_79><loc_34></location>We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables.</paragraph>
<paragraph><location><page_1><loc_22><loc_16><loc_79><loc_22></location>Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation.</paragraph>

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