fix(ocr): tesseract support mis-oriented documents

Signed-off-by: Clément Doumouro <clement.doumouro@gmail.com>

docling/models/tesseract_ocr_cli_model.py

@@ -6,10 +6,9 @@ import tempfile
 from collections.abc import Iterable
 from pathlib import Path
 from subprocess import DEVNULL, PIPE, Popen
-from typing import List, Optional, Tuple, Type
+from typing import List, Optional, Tuple, Type, cast
 
 import pandas as pd
-from docling_core.types.doc import BoundingBox, CoordOrigin
 from docling_core.types.doc.page import BoundingRectangle, TextCell
 
 from docling.datamodel.base_models import Page
@@ -21,7 +20,12 @@ from docling.datamodel.pipeline_options import (
 )
 from docling.datamodel.settings import settings
 from docling.models.base_ocr_model import BaseOcrModel
-from docling.utils.ocr_utils import map_tesseract_script
+from docling.utils.ocr_utils import (
+    Box,
+    map_tesseract_script,
+    parse_tesseract_orientation,
+    tesseract_box_to_bounding_rectangle,
+)
 from docling.utils.profiling import TimeRecorder
 
 _log = logging.getLogger(__name__)
@@ -93,14 +97,13 @@ class TesseractOcrCliModel(BaseOcrModel):
 
         return name, version
 
-    def _run_tesseract(self, ifilename: str):
+    def _run_tesseract(self, ifilename: str, osd: pd.DataFrame):
         r"""
         Run tesseract CLI
         """
         cmd = [self.options.tesseract_cmd]
-
         if "auto" in self.options.lang:
-            lang = self._detect_language(ifilename)
+            lang = self._parse_language(osd)
             if lang is not None:
                 cmd.append("-l")
                 cmd.append(lang)
@@ -139,11 +142,10 @@ class TesseractOcrCliModel(BaseOcrModel):
 
         return df_filtered
 
-    def _detect_language(self, ifilename: str):
+    def _perform_osd(self, ifilename: str) -> pd.DataFrame:
         r"""
         Run tesseract in PSM 0 mode to detect the language
         """
-        assert self._tesseract_languages is not None
 
         cmd = [self.options.tesseract_cmd]
         cmd.extend(["--psm", "0", "-l", "osd", ifilename, "stdout"])
@@ -154,7 +156,11 @@ class TesseractOcrCliModel(BaseOcrModel):
         df_detected = pd.read_csv(
             io.StringIO(decoded_data), sep=":", header=None, names=["key", "value"]
         )
-        scripts = df_detected.loc[df_detected["key"] == "Script"].value.tolist()
+        return df_detected
+
+    def _parse_language(self, df_osd: pd.DataFrame) -> Optional[str]:
+        assert self._tesseract_languages is not None
+        scripts = df_osd.loc[df_osd["key"] == "Script"].value.tolist()
         if len(scripts) == 0:
             _log.warning("Tesseract cannot detect the script of the page")
             return None
@@ -225,8 +231,14 @@ class TesseractOcrCliModel(BaseOcrModel):
                             ) as image_file:
                                 fname = image_file.name
                                 high_res_image.save(image_file)
-
+                            df_osd = self._perform_osd(fname)
-                            df_result = self._run_tesseract(fname)
+                            doc_orientation = _parse_orientation(df_osd)
+                            if doc_orientation != 0:
+                                high_res_image = high_res_image.rotate(
+                                    doc_orientation, expand=True
+                                )
+                                high_res_image.save(fname)
+                            df_result = self._run_tesseract(fname, df_osd)
                         finally:
                             if os.path.exists(fname):
                                 os.remove(fname)
@@ -238,13 +250,22 @@ class TesseractOcrCliModel(BaseOcrModel):
                             text = row["text"]
                             conf = row["conf"]
 
-                            l = float(row["left"])  # noqa: E741
+                            rotated_bbox = (
-                            b = float(row["top"])
+                                row["left"],
-                            w = float(row["width"])
+                                row["top"],
-                            h = float(row["height"])
+                                row["width"],
-
+                                row["height"],
-                            t = b + h
+                            )
-                            r = l + w
+                            rotated_bbox = cast(
+                                Box, tuple(float(c) for c in rotated_bbox)
+                            )
+                            rect = tesseract_box_to_bounding_rectangle(
+                                rotated_bbox,
+                                offset=ocr_rect,
+                                scale=self.scale,
+                                orientation=doc_orientation,
+                                rotated_image_size=high_res_image.size,
+                            )
 
                             cell = TextCell(
                                 index=ix,
@@ -252,17 +273,7 @@ class TesseractOcrCliModel(BaseOcrModel):
                                 orig=str(text),
                                 from_ocr=True,
                                 confidence=conf / 100.0,
-                                rect=BoundingRectangle.from_bounding_box(
+                                rect=rect,
-                                    BoundingBox.from_tuple(
-                                        coord=(
-                                            (l / self.scale) + ocr_rect.l,
-                                            (b / self.scale) + ocr_rect.t,
-                                            (r / self.scale) + ocr_rect.l,
-                                            (t / self.scale) + ocr_rect.t,
-                                        ),
-                                        origin=CoordOrigin.TOPLEFT,
-                                    )
-                                ),
                             )
                             all_ocr_cells.append(cell)
 
@@ -278,3 +289,9 @@ class TesseractOcrCliModel(BaseOcrModel):
     @classmethod
     def get_options_type(cls) -> Type[OcrOptions]:
         return TesseractCliOcrOptions
+
+
+def _parse_orientation(df_osd: pd.DataFrame) -> int:
+    orientations = df_osd.loc[df_osd["key"] == "Orientation in degrees"].value.tolist()
+    orientation = parse_tesseract_orientation(orientations[0].strip())
+    return orientation

docling/models/tesseract_ocr_model.py

@@ -3,10 +3,9 @@ from __future__ import annotations
 import logging
 from collections.abc import Iterable
 from pathlib import Path
-from typing import Optional, Type
+from typing import Dict, Iterable, Optional, Type
 
-from docling_core.types.doc import BoundingBox, CoordOrigin
+from docling_core.types.doc.page import TextCell
-from docling_core.types.doc.page import BoundingRectangle, TextCell
 
 from docling.datamodel.base_models import Page
 from docling.datamodel.document import ConversionResult
@@ -17,7 +16,11 @@ from docling.datamodel.pipeline_options import (
 )
 from docling.datamodel.settings import settings
 from docling.models.base_ocr_model import BaseOcrModel
-from docling.utils.ocr_utils import map_tesseract_script
+from docling.utils.ocr_utils import (
+    map_tesseract_script,
+    parse_tesseract_orientation,
+    tesseract_box_to_bounding_rectangle,
+)
 from docling.utils.profiling import TimeRecorder
 
 _log = logging.getLogger(__name__)
@@ -95,13 +98,13 @@ class TesseractOcrModel(BaseOcrModel):
 
             if lang == "auto":
                 self.reader = tesserocr.PyTessBaseAPI(**tesserocr_kwargs)
-                self.osd_reader = tesserocr.PyTessBaseAPI(
-                    **{"lang": "osd", "psm": tesserocr.PSM.OSD_ONLY} | tesserocr_kwargs
-                )
             else:
                 self.reader = tesserocr.PyTessBaseAPI(
                     **{"lang": lang} | tesserocr_kwargs,
                 )
+            self.osd_reader = tesserocr.PyTessBaseAPI(
+                **{"lang": "osd", "psm": tesserocr.PSM.OSD_ONLY} | tesserocr_kwargs
+            )
             self.reader_RIL = tesserocr.RIL
 
     def __del__(self):
@@ -125,6 +128,7 @@ class TesseractOcrModel(BaseOcrModel):
             else:
                 with TimeRecorder(conv_res, "ocr"):
                     assert self.reader is not None
+                    assert self.osd_reader is not None
                     assert self._tesserocr_languages is not None
 
                     ocr_rects = self.get_ocr_rects(page)
@@ -139,16 +143,17 @@ class TesseractOcrModel(BaseOcrModel):
                         )
 
                         local_reader = self.reader
-                        if "auto" in self.options.lang:
-                            assert self.osd_reader is not None
-
                         self.osd_reader.SetImage(high_res_image)
                         osd = self.osd_reader.DetectOrientationScript()
-
                         # No text, probably
                         if osd is None:
                             continue
-
+                        doc_orientation = parse_tesseract_orientation(osd["orient_deg"])
+                        if doc_orientation != 0:
+                            high_res_image = high_res_image.rotate(
+                                doc_orientation, expand=True
+                            )
+                        if "auto" in self.options.lang:
                             script = osd["script_name"]
                             script = map_tesseract_script(script)
                             lang = f"{self.script_prefix}{script}"
@@ -188,11 +193,14 @@ class TesseractOcrModel(BaseOcrModel):
                             # Extract text within the bounding box
                             text = local_reader.GetUTF8Text().strip()
                             confidence = local_reader.MeanTextConf()
-                            left = box["x"] / self.scale
+                            rotated_bbox = (box["x"], box["y"], box["w"], box["h"])
-                            bottom = box["y"] / self.scale
+                            rect = tesseract_box_to_bounding_rectangle(
-                            right = (box["x"] + box["w"]) / self.scale
+                                rotated_bbox,
-                            top = (box["y"] + box["h"]) / self.scale
+                                offset=ocr_rect,
-
+                                scale=self.scale,
+                                orientation=doc_orientation,
+                                rotated_image_size=high_res_image.size,
+                            )
                             cells.append(
                                 TextCell(
                                     index=ix,
@@ -200,12 +208,7 @@ class TesseractOcrModel(BaseOcrModel):
                                     orig=text,
                                     from_ocr=True,
                                     confidence=confidence,
-                                    rect=BoundingRectangle.from_bounding_box(
+                                    rect=rect,
-                                        BoundingBox.from_tuple(
-                                            coord=(left, top, right, bottom),
-                                            origin=CoordOrigin.TOPLEFT,
-                                        ),
-                                    ),
                                 )
                             )
 

docling/utils/ocr_utils.py

@@ -1,3 +1,15 @@
+from typing import Optional, Tuple
+
+from docling_core.types.doc import BoundingBox, CoordOrigin
+from docling_core.types.doc.page import BoundingRectangle
+
+_TESSERACT_ORIENTATIONS = {0, 90, 180, 270}
+
+Point = Tuple[float, float]
+Box = Tuple[float, float, float, float]
+Size = Tuple[int, int]
+
+
 def map_tesseract_script(script: str) -> str:
     r""" """
     if script == "Katakana" or script == "Hiragana":
@@ -7,3 +19,75 @@ def map_tesseract_script(script: str) -> str:
     elif script == "Korean":
         script = "Hangul"
     return script
+
+
+def reverse_tesseract_preprocessing_rotation(
+    box: Box, orientation: int, rotated_im_size: Size
+) -> tuple[Point, Point, Point, Point]:
+    l, t, w, h = box
+    rotated_w, rotated_h = rotated_im_size
+    if orientation == 0:
+        return (l, t), (l + w, t), (l + w, t + h), (l, t + h)
+    if orientation == 90:
+        x0 = rotated_h - t
+        y0 = l
+        return (x0, y0), (x0, y0 + w), (x0 - h, y0 + w), (x0 - h, y0)
+    if orientation == 180:
+        x0 = rotated_w - l
+        y0 = rotated_h - t
+        return (x0, y0), (x0 - w, y0), (x0 - w, y0 - h), (x0, y0 - h)
+    if orientation == 270:
+        x0 = t
+        y0 = rotated_w - l
+        return (x0, y0), (x0, y0 - w), (x0 + h, y0 - w), (x0 + h, y0)
+    msg = (
+        f"invalid tesseract document orientation {orientation}, "
+        f"expected orientation: {sorted(_TESSERACT_ORIENTATIONS)}"
+    )
+    raise ValueError(msg)
+
+
+def parse_tesseract_orientation(orientation: str) -> int:
+    parsed = int(orientation)
+    if parsed not in _TESSERACT_ORIENTATIONS:
+        msg = (
+            f"invalid tesseract document orientation {orientation}, "
+            f"expected orientation: {sorted(_TESSERACT_ORIENTATIONS)}"
+        )
+        raise ValueError(msg)
+    return parsed
+
+
+def tesseract_box_to_bounding_rectangle(
+    box: Box,
+    *,
+    offset: Optional[BoundingBox] = None,
+    scale: float,
+    orientation: int,
+    rotated_image_size: Size,
+) -> BoundingRectangle:
+    # box is in the top, left, height, width format + top left orientation
+    r_0, r_1, r_2, r_3 = reverse_tesseract_preprocessing_rotation(
+        box, orientation, rotated_image_size
+    )
+    rect = BoundingRectangle(
+        r_x0=r_0[0] / scale,
+        r_y0=r_0[1] / scale,
+        r_x1=r_1[0] / scale,
+        r_y1=r_1[1] / scale,
+        r_x2=r_2[0] / scale,
+        r_y2=r_2[1] / scale,
+        r_x3=r_3[0] / scale,
+        r_y3=r_3[1] / scale,
+        coord_origin=CoordOrigin.TOPLEFT,
+    )
+    if offset is not None:
+        rect.r_x0 += offset.l
+        rect.r_x1 += offset.l
+        rect.r_x2 += offset.l
+        rect.r_x3 += offset.l
+        rect.r_y0 += offset.t
+        rect.r_y1 += offset.t
+        rect.r_y2 += offset.t
+        rect.r_y3 += offset.t
+    return rect

tests/data_scanned/groundtruth/docling_v1/ocr_test_rotated.doctags.txt

@@ -0,0 +1,3 @@
+<document>
+<paragraph><location><page_1><loc_16><loc_12><loc_18><loc_26></location>package</paragraph>
+</document>

tests/data_scanned/groundtruth/docling_v1/ocr_test_rotated.json

@@ -0,0 +1 @@
+{"_name": "", "type": "pdf-document", "description": {"title": null, "abstract": null, "authors": null, "affiliations": null, "subjects": null, "keywords": null, "publication_date": null, "languages": null, "license": null, "publishers": null, "url_refs": null, "references": null, "publication": null, "reference_count": null, "citation_count": null, "citation_date": null, "advanced": null, "analytics": null, "logs": [], "collection": null, "acquisition": null}, "file-info": {"filename": "ocr_test_rotated.pdf", "filename-prov": null, "document-hash": "4a282813d93824eaa9bc2a0b2a0d6d626ecc8f5f380bd1320e2dd3e8e53c2ba6", "#-pages": 1, "collection-name": null, "description": null, "page-hashes": [{"hash": "f8a4dc72d8b159f69d0bc968b97f3fb9e0ac59dcb3113492432755835935d9b3", "model": "default", "page": 1}]}, "main-text": [{"prov": [{"bbox": [131.21306574279092, 74.12495603322407, 152.19606490864376, 154.19400205373182], "page": 1, "span": [0, 7], "__ref_s3_data": null}], "text": "package", "type": "paragraph", "payload": null, "name": "Text", "font": null}], "figures": [], "tables": [], "bitmaps": null, "equations": [], "footnotes": [], "page-dimensions": [{"height": 595.201171875, "page": 1, "width": 841.9216918945312}], "page-footers": [], "page-headers": [], "_s3_data": null, "identifiers": null}

tests/data_scanned/groundtruth/docling_v1/ocr_test_rotated.md

@@ -0,0 +1 @@
+package

tests/data_scanned/groundtruth/docling_v1/ocr_test_rotated.pages.json

@@ -0,0 +1 @@
+[{"page_no": 0, "size": {"width": 841.9216918945312, "height": 595.201171875}, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 77.10171546422428, "t": 89.23887398109309, "r": 96.6831586150625, "b": 520.7638577050515, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 100.55299576256091, "t": 89.12381765643227, "r": 124.91101654503161, "b": 523.3155494272656, "coord_origin": "TOPLEFT"}}, {"id": 2, "text": "package", "bbox": {"l": 131.21306574279092, "t": 441.0071698212682, "r": 152.19606490864376, "b": 521.0762158417759, "coord_origin": "TOPLEFT"}}], "predictions": {"layout": {"clusters": [{"id": 0, "label": "page_header", "bbox": {"l": 77.10171546422428, "t": 89.12381765643227, "r": 124.91101654503161, "b": 523.3155494272656, "coord_origin": "TOPLEFT"}, "confidence": 0.6016772389411926, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 77.10171546422428, "t": 89.23887398109309, "r": 96.6831586150625, "b": 520.7638577050515, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 100.55299576256091, "t": 89.12381765643227, "r": 124.91101654503161, "b": 523.3155494272656, "coord_origin": "TOPLEFT"}}], "children": []}, {"id": 1, "label": "text", "bbox": {"l": 131.21306574279092, "t": 441.0071698212682, "r": 152.19606490864376, "b": 521.0762158417759, "coord_origin": "TOPLEFT"}, "confidence": 0.5234212875366211, "cells": [{"id": 2, "text": "package", "bbox": {"l": 131.21306574279092, "t": 441.0071698212682, "r": 152.19606490864376, "b": 521.0762158417759, "coord_origin": "TOPLEFT"}}], "children": []}]}, "tablestructure": {"table_map": {}}, "figures_classification": null, "equations_prediction": null, "vlm_response": null}, "assembled": {"elements": [{"label": "page_header", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "page_header", "bbox": {"l": 77.10171546422428, "t": 89.12381765643227, "r": 124.91101654503161, "b": 523.3155494272656, "coord_origin": "TOPLEFT"}, "confidence": 0.6016772389411926, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 77.10171546422428, "t": 89.23887398109309, "r": 96.6831586150625, "b": 520.7638577050515, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 100.55299576256091, "t": 89.12381765643227, "r": 124.91101654503161, "b": 523.3155494272656, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained"}, {"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 131.21306574279092, "t": 441.0071698212682, "r": 152.19606490864376, "b": 521.0762158417759, "coord_origin": "TOPLEFT"}, "confidence": 0.5234212875366211, "cells": [{"id": 2, "text": "package", "bbox": {"l": 131.21306574279092, "t": 441.0071698212682, "r": 152.19606490864376, "b": 521.0762158417759, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "package"}], "body": [{"label": "text", "id": 1, "page_no": 0, "cluster": {"id": 1, "label": "text", "bbox": {"l": 131.21306574279092, "t": 441.0071698212682, "r": 152.19606490864376, "b": 521.0762158417759, "coord_origin": "TOPLEFT"}, "confidence": 0.5234212875366211, "cells": [{"id": 2, "text": "package", "bbox": {"l": 131.21306574279092, "t": 441.0071698212682, "r": 152.19606490864376, "b": 521.0762158417759, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "package"}], "headers": [{"label": "page_header", "id": 0, "page_no": 0, "cluster": {"id": 0, "label": "page_header", "bbox": {"l": 77.10171546422428, "t": 89.12381765643227, "r": 124.91101654503161, "b": 523.3155494272656, "coord_origin": "TOPLEFT"}, "confidence": 0.6016772389411926, "cells": [{"id": 0, "text": "Docling bundles PDF document conversion to", "bbox": {"l": 77.10171546422428, "t": 89.23887398109309, "r": 96.6831586150625, "b": 520.7638577050515, "coord_origin": "TOPLEFT"}}, {"id": 1, "text": "JSON and Markdown in an easy self contained", "bbox": {"l": 100.55299576256091, "t": 89.12381765643227, "r": 124.91101654503161, "b": 523.3155494272656, "coord_origin": "TOPLEFT"}}], "children": []}, "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained"}]}}]

tests/data_scanned/groundtruth/docling_v1/ocr_test_rotated_180.doctags.txt

@@ -0,0 +1,4 @@
+<document>
+<paragraph><location><page_1><loc_75><loc_16><loc_88><loc_18></location>package</paragraph>
+<paragraph><location><page_1><loc_15><loc_9><loc_88><loc_15></location>Docling bundles PDF document conversion to JSON and Markdown in an easy self contained</paragraph>
+</document>

tests/data_scanned/groundtruth/docling_v1/ocr_test_rotated_180.json

@@ -0,0 +1 @@
+{"_name": "", "type": "pdf-document", "description": {"title": null, "abstract": null, "authors": null, "affiliations": null, "subjects": null, "keywords": null, "publication_date": null, "languages": null, "license": null, "publishers": null, "url_refs": null, "references": null, "publication": null, "reference_count": null, "citation_count": null, "citation_date": null, "advanced": null, "analytics": null, "logs": [], "collection": null, "acquisition": null}, "file-info": {"filename": "ocr_test_rotated_180.pdf", "filename-prov": null, "document-hash": "a9cbfe0f2a71171face9ee31d2347ca4195649670ad75680520d67d4a863f982", "#-pages": 1, "collection-name": null, "description": null, "page-hashes": [{"hash": "baca27070f05dd84cf0903ded39bcf0fc1fa6ef0ac390e79cf8ba90c8c33ba49", "model": "default", "page": 1}]}, "main-text": [{"prov": [{"bbox": [444.6666666666667, 131.58835856119788, 521.6666666666666, 150.25502522786462], "page": 1, "span": [0, 7], "__ref_s3_data": null}], "text": "package", "type": "paragraph", "payload": null, "name": "Text", "font": null}, {"prov": [{"bbox": [92.0, 77.92169189453125, 523.0, 123.25502522786462], "page": 1, "span": [0, 86], "__ref_s3_data": null}], "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained", "type": "paragraph", "payload": null, "name": "Text", "font": null}], "figures": [], "tables": [], "bitmaps": null, "equations": [], "footnotes": [], "page-dimensions": [{"height": 841.9216918945312, "page": 1, "width": 595.201171875}], "page-footers": [], "page-headers": [], "_s3_data": null, "identifiers": null}

tests/data_scanned/groundtruth/docling_v1/ocr_test_rotated_180.md

@@ -0,0 +1,3 @@
+package
+
+Docling bundles PDF document conversion to JSON and Markdown in an easy self contained

tests/data_scanned/groundtruth/docling_v1/ocr_test_rotated_270.doctags.txt

@@ -0,0 +1,3 @@
+<document>
+<paragraph><location><page_1><loc_82><loc_75><loc_84><loc_88></location>package</paragraph>
+</document>

tests/data_scanned/groundtruth/docling_v1/ocr_test_rotated_270.json

@@ -0,0 +1 @@
+{"_name": "", "type": "pdf-document", "description": {"title": null, "abstract": null, "authors": null, "affiliations": null, "subjects": null, "keywords": null, "publication_date": null, "languages": null, "license": null, "publishers": null, "url_refs": null, "references": null, "publication": null, "reference_count": null, "citation_count": null, "citation_date": null, "advanced": null, "analytics": null, "logs": [], "collection": null, "acquisition": null}, "file-info": {"filename": "ocr_test_rotated_270.pdf", "filename-prov": null, "document-hash": "52f54e7183bdb73aa3713c7b169baca93e276963a138418c26e7d6a1ea128f14", "#-pages": 1, "collection-name": null, "description": null, "page-hashes": [{"hash": "59bc9ddba89e7b008185dd16d384493beb034686e5670546786390c5d237a304", "model": "default", "page": 1}]}, "main-text": [{"prov": [{"bbox": [691.6666666666666, 444.53450520833337, 710.3333333333334, 521.5345052083334], "page": 1, "span": [0, 7], "__ref_s3_data": null}], "text": "package", "type": "paragraph", "payload": null, "name": "Text", "font": null}], "figures": [], "tables": [], "bitmaps": null, "equations": [], "footnotes": [], "page-dimensions": [{"height": 595.201171875, "page": 1, "width": 841.9216918945312}], "page-footers": [], "page-headers": [], "_s3_data": null, "identifiers": null}

tests/data_scanned/groundtruth/docling_v1/ocr_test_rotated_270.md

@@ -0,0 +1 @@
+package

tests/data_scanned/groundtruth/docling_v1/ocr_test_rotated_90.doctags.txt

@@ -0,0 +1,3 @@
+<document>
+<paragraph><location><page_1><loc_16><loc_12><loc_18><loc_25></location>package</paragraph>
+</document>

tests/data_scanned/groundtruth/docling_v1/ocr_test_rotated_90.json

@@ -0,0 +1 @@
+{"_name": "", "type": "pdf-document", "description": {"title": null, "abstract": null, "authors": null, "affiliations": null, "subjects": null, "keywords": null, "publication_date": null, "languages": null, "license": null, "publishers": null, "url_refs": null, "references": null, "publication": null, "reference_count": null, "citation_count": null, "citation_date": null, "advanced": null, "analytics": null, "logs": [], "collection": null, "acquisition": null}, "file-info": {"filename": "ocr_test_rotated_90.pdf", "filename-prov": null, "document-hash": "4a282813d93824eaa9bc2a0b2a0d6d626ecc8f5f380bd1320e2dd3e8e53c2ba6", "#-pages": 1, "collection-name": null, "description": null, "page-hashes": [{"hash": "f8a4dc72d8b159f69d0bc968b97f3fb9e0ac59dcb3113492432755835935d9b3", "model": "default", "page": 1}]}, "main-text": [{"prov": [{"bbox": [131.66666666666666, 73.53450520833337, 150.33333333333334, 150.53450520833331], "page": 1, "span": [0, 7], "__ref_s3_data": null}], "text": "package", "type": "paragraph", "payload": null, "name": "Text", "font": null}], "figures": [], "tables": [], "bitmaps": null, "equations": [], "footnotes": [], "page-dimensions": [{"height": 595.201171875, "page": 1, "width": 841.9216918945312}], "page-footers": [], "page-headers": [], "_s3_data": null, "identifiers": null}

tests/data_scanned/groundtruth/docling_v1/ocr_test_rotated_90.md

@@ -0,0 +1 @@
+package

tests/data_scanned/groundtruth/docling_v2/ocr_test_rotated_180.doctags.txt

@@ -0,0 +1,3 @@
+<doctag><text><loc_374><loc_411><loc_438><loc_422>package</text>
+<text><loc_77><loc_427><loc_439><loc_454>Docling bundles PDF document conversion to JSON and Markdown in an easy self contained</text>
+</doctag>

tests/data_scanned/groundtruth/docling_v2/ocr_test_rotated_180.json

@@ -0,0 +1 @@
+{"schema_name": "DoclingDocument", "version": "1.3.0", "name": "ocr_test_rotated_180", "origin": {"mimetype": "application/pdf", "binary_hash": 2530576989861832966, "filename": "ocr_test_rotated_180.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "content_layer": "furniture", "name": "_root_", "label": "unspecified"}, "body": {"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}, {"cref": "#/texts/1"}], "content_layer": "body", "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 444.6666666666667, "t": 150.25502522786462, "r": 521.6666666666666, "b": 131.58835856119788, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "package", "text": "package", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 92.0, "t": 123.25502522786462, "r": 523.0, "b": 77.92169189453125, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 86]}], "orig": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained", "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained", "formatting": null, "hyperlink": null}], "pictures": [], "tables": [], "key_value_items": [], "form_items": [], "pages": {"1": {"size": {"width": 595.201171875, "height": 841.9216918945312}, "image": null, "page_no": 1}}}

tests/data_scanned/groundtruth/docling_v2/ocr_test_rotated_180.md

@@ -0,0 +1,3 @@
+package
+
+Docling bundles PDF document conversion to JSON and Markdown in an easy self contained

tests/data_scanned/groundtruth/docling_v2/ocr_test_rotated_270.doctags.txt

@@ -0,0 +1,3 @@
+<doctag><page_header><loc_427><loc_61><loc_454><loc_423>Docling bundles PDF document conversion to JSON and Markdown in an easy self contained</page_header>
+<text><loc_411><loc_62><loc_422><loc_127>package</text>
+</doctag>

tests/data_scanned/groundtruth/docling_v2/ocr_test_rotated_270.json

@@ -0,0 +1 @@
+{"schema_name": "DoclingDocument", "version": "1.3.0", "name": "ocr_test_rotated_270", "origin": {"mimetype": "application/pdf", "binary_hash": 10890858393843077593, "filename": "ocr_test_rotated_270.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "content_layer": "furniture", "name": "_root_", "label": "unspecified"}, "body": {"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}, {"cref": "#/texts/1"}], "content_layer": "body", "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 718.6666666666666, "t": 522.8678385416666, "r": 764.0, "b": 91.86783854166669, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 86]}], "orig": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained", "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 691.6666666666666, "t": 521.5345052083334, "r": 710.3333333333334, "b": 444.53450520833337, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "package", "text": "package", "formatting": null, "hyperlink": null}], "pictures": [], "tables": [], "key_value_items": [], "form_items": [], "pages": {"1": {"size": {"width": 841.9216918945312, "height": 595.201171875}, "image": null, "page_no": 1}}}

tests/data_scanned/groundtruth/docling_v2/ocr_test_rotated_270.md

@@ -0,0 +1 @@
+package

tests/data_scanned/groundtruth/docling_v2/ocr_test_rotated_90.doctags.txt

@@ -0,0 +1,3 @@
+<doctag><page_header><loc_46><loc_77><loc_73><loc_439>Docling bundles PDF document conversion to JSON and Markdown in an easy self contained</page_header>
+<text><loc_78><loc_374><loc_89><loc_438>package</text>
+</doctag>

tests/data_scanned/groundtruth/docling_v2/ocr_test_rotated_90.json

@@ -0,0 +1 @@
+{"schema_name": "DoclingDocument", "version": "1.3.0", "name": "ocr_test_rotated_90", "origin": {"mimetype": "application/pdf", "binary_hash": 6989291015361162334, "filename": "ocr_test_rotated_90.pdf", "uri": null}, "furniture": {"self_ref": "#/furniture", "parent": null, "children": [], "content_layer": "furniture", "name": "_root_", "label": "unspecified"}, "body": {"self_ref": "#/body", "parent": null, "children": [{"cref": "#/texts/0"}, {"cref": "#/texts/1"}], "content_layer": "body", "name": "_root_", "label": "unspecified"}, "groups": [], "texts": [{"self_ref": "#/texts/0", "parent": {"cref": "#/body"}, "children": [], "content_layer": "furniture", "label": "page_header", "prov": [{"page_no": 1, "bbox": {"l": 78.0, "t": 503.201171875, "r": 123.33333333333333, "b": 72.201171875, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 86]}], "orig": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained", "text": "Docling bundles PDF document conversion to JSON and Markdown in an easy self contained", "formatting": null, "hyperlink": null}, {"self_ref": "#/texts/1", "parent": {"cref": "#/body"}, "children": [], "content_layer": "body", "label": "text", "prov": [{"page_no": 1, "bbox": {"l": 131.66666666666666, "t": 150.53450520833331, "r": 150.33333333333334, "b": 73.53450520833337, "coord_origin": "BOTTOMLEFT"}, "charspan": [0, 7]}], "orig": "package", "text": "package", "formatting": null, "hyperlink": null}], "pictures": [], "tables": [], "key_value_items": [], "form_items": [], "pages": {"1": {"size": {"width": 841.9216918945312, "height": 595.201171875}, "image": null, "page_no": 1}}}

tests/data_scanned/groundtruth/docling_v2/ocr_test_rotated_90.md

@@ -0,0 +1 @@
+package

tests/test_e2e_ocr_conversion.py

@@ -1,6 +1,6 @@
 import sys
 from pathlib import Path
-from typing import List
+from typing import List, Tuple
 
 from docling.backend.docling_parse_backend import DoclingParseDocumentBackend
 from docling.datamodel.base_models import InputFormat
@@ -56,33 +56,35 @@ def get_converter(ocr_options: OcrOptions):
 def test_e2e_conversions():
     pdf_paths = get_pdf_paths()
 
-    engines: List[OcrOptions] = [
+    engines: List[Tuple[OcrOptions, bool]] = [
-        EasyOcrOptions(),
+        (EasyOcrOptions(), False),
-        TesseractOcrOptions(),
+        (TesseractOcrOptions(), True),
-        TesseractCliOcrOptions(),
+        (TesseractCliOcrOptions(), True),
-        EasyOcrOptions(force_full_page_ocr=True),
+        (EasyOcrOptions(force_full_page_ocr=True), False),
-        TesseractOcrOptions(force_full_page_ocr=True),
+        (TesseractOcrOptions(force_full_page_ocr=True), True),
-        TesseractOcrOptions(force_full_page_ocr=True, lang=["auto"]),
+        (TesseractOcrOptions(force_full_page_ocr=True, lang=["auto"]), True),
-        TesseractCliOcrOptions(force_full_page_ocr=True),
+        (TesseractCliOcrOptions(force_full_page_ocr=True), True),
-        TesseractCliOcrOptions(force_full_page_ocr=True, lang=["auto"]),
+        (TesseractCliOcrOptions(force_full_page_ocr=True, lang=["auto"]), True),
     ]
 
     # rapidocr is only available for Python >=3.6,<3.13
     if sys.version_info < (3, 13):
-        engines.append(RapidOcrOptions())
+        engines.append((RapidOcrOptions(), False))
-        engines.append(RapidOcrOptions(force_full_page_ocr=True))
+        engines.append((RapidOcrOptions(force_full_page_ocr=True), False))
 
     # only works on mac
     if "darwin" == sys.platform:
-        engines.append(OcrMacOptions())
+        engines.append((OcrMacOptions(), True))
-        engines.append(OcrMacOptions(force_full_page_ocr=True))
+        engines.append((OcrMacOptions(force_full_page_ocr=True), True))
 
-    for ocr_options in engines:
+    for ocr_options, supports_rotation in engines:
         print(
             f"Converting with ocr_engine: {ocr_options.kind}, language: {ocr_options.lang}"
         )
         converter = get_converter(ocr_options=ocr_options)
         for pdf_path in pdf_paths:
+            if not supports_rotation and "rotated" in pdf_path.name:
+                continue
             print(f"converting {pdf_path}")
 
             doc_result: ConversionResult = converter.convert(pdf_path)