Fixed:

• continue processing when no columns detected but text regions exist
• convert marginalia to main text if no main text is present
• reset deskewing angle to 0° when text covers <30% image area and detected angle >45°
• 🔥 polygons: avoid invalid paths (use Polygon.buffer() instead of dilation etc.)
• return_boxes_of_images_by_order_of_reading_new: avoid Numpy.dtype mismatch, simplify
• return_boxes_of_images_by_order_of_reading_new: log any exceptions instead of ignoring
• filter_contours_without_textline_inside: avoid removing from duplicate lists twice
• get_marginals: exit early if no peaks found to avoid spurious overlap mask
• get_smallest_skew: after shifting search range of rotation angle, use overall best result
• Dockerfile: fix CUDA installation (cuDNN contested between Torch and TF due to extra OCR)
• OCR: re-instate missing methods and fix utils_ocr function calls
• mbreorder/enhancement CLIs: missing imports
• 🔥 writer: SeparatorRegion needs SeparatorRegionType (not ImageRegionType), f458e3
• tests: switch from pytest-subtests to parametrize so we can use pytest-isolate
  (so CUDA memory gets freed between tests if running on GPU)
• Prevent OOM GPU error by avoiding loading the region_fl model, #199
• XML output: encoding should be utf-8, not utf8, #196, #197
• join_polygons always returning Polygon, not MultiPolygon, #203

Added:

• 🔥 eynollah-training CLI and docs for training the models, #187, #193, https://github.com/qurator-spk/sbb_pixelwise_segmentation/tree/unifying-training-models
• 🔥 layout CLI: new option --model_version to override default choices
• test coverage for OCR options in layout
• test coverage for table detection in layout
• CI linting with ruff

Changed:

• polygons: slightly widen for regions and lines, increase for separators
• various refactorings, some code style and identifier improvements
• deskewing/multiprocessing: switch back to ProcessPoolExecutor (faster),
  but use shared memory if necessary, and switch back from loky to stdlib,
  and shutdown in del() instead of atexit
• 🔥 OCR: switch CNN-RNN model to 20250930 version compatible with TF 2.12 on CPU, too
• OCR: allow running -tr without -fl, too
• 🔥 writer: use @type='heading' instead of 'header' for headings
• 🔥 performance gains via refactoring (simplification, less copy-code, vectorization,
  avoiding unused calculations, avoiding unnecessary 3-channel image operations)
• 🔥 heuristic reading order detection: many improvements
  • contour vs splitter box matching:
    • contour must be contained in box exactly instead of heuristics
    • make fallback center matching, center must be contained in box
  • original vs deskewed contour matching:
    • same min-area filter on both sides
    • similar area score in addition to center proximity
    • avoid duplicate and missing mappings by allowing N:M
      matches and splitting+joining where necessary
• CI: update+improve model caching

Merged PRs

• CD: master is now main by @bertsky in #185
• 📝 extend changelog for v0.5.0 by @kba in #186
• new attempt at #173 (valid polygons, faster deskewing, various fixes) by @bertsky in #192
• XML encoding should be utf-8 not utf8 by @kba in #197
• Fix overflow by @bertsky in #199
• Prepare v0.6.0rc2 by @kba in #200
• Training installation by @kba in #193
• Integrate training from sbb pixelwise segmentation by @kba in #187
• join_polygons: try to catch rare case of MultiPolygon by @kba in #203

Full Changelog: v0.5.0...v0.6.0

Fixed:

• Prevent OOM GPU error by avoiding loading the region_fl model, #199
• XML output: encoding should be utf-8, not utf8, #196, #197

Fixed:

• continue processing when no columns detected but text regions exist
• convert marginalia to main text if no main text is present
• reset deskewing angle to 0° when text covers <30% image area and detected angle >45°
• 🔥 polygons: avoid invalid paths (use Polygon.buffer() instead of dilation etc.)
• return_boxes_of_images_by_order_of_reading_new: avoid Numpy.dtype mismatch, simplify
• return_boxes_of_images_by_order_of_reading_new: log any exceptions instead of ignoring
• filter_contours_without_textline_inside: avoid removing from duplicate lists twice
• get_marginals: exit early if no peaks found to avoid spurious overlap mask
• get_smallest_skew: after shifting search range of rotation angle, use overall best result
• Dockerfile: fix CUDA installation (cuDNN contested between Torch and TF due to extra OCR)
• OCR: re-instate missing methods and fix utils_ocr function calls
• mbreorder/enhancement CLIs: missing imports
• 🔥 writer: SeparatorRegion needs SeparatorRegionType (not ImageRegionType)
  f458e3e
• tests: switch from pytest-subtests to parametrize so we can use pytest-isolate
  (so CUDA memory gets freed between tests if running on GPU)

Added:

• 🔥 layout CLI: new option --model_version to override default choices
• test coverage for OCR options in layout
• test coverage for table detection in layout
• CI linting with ruff

Changed:

• polygons: slightly widen for regions and lines, increase for separators
• various refactorings, some code style and identifier improvements
• deskewing/multiprocessing: switch back to ProcessPoolExecutor (faster),
  but use shared memory if necessary, and switch back from loky to stdlib,
  and shutdown in del() instead of atexit
• 🔥 OCR: switch CNN-RNN model to 20250930 version compatible with TF 2.12 on CPU, too
• OCR: allow running -tr without -fl, too
• 🔥 writer: use @type='heading' instead of 'header' for headings
• 🔥 performance gains via refactoring (simplification, less copy-code, vectorization,
  avoiding unused calculations, avoiding unnecessary 3-channel image operations)
• 🔥 heuristic reading order detection: many improvements
  • contour vs splitter box matching:
    • contour must be contained in box exactly instead of heuristics
    • make fallback center matching, center must be contained in box
  • original vs deskewed contour matching:
    • same min-area filter on both sides
    • similar area score in addition to center proximity
    • avoid duplicate and missing mappings by allowing N:M
      matches and splitting+joining where necessary
• CI: update+improve model caching

Merged PRs

• CD: master is now main by @bertsky in #185
• 📝 extend changelog for v0.5.0 by @vahidrezanezhad in #186
• new attempt at #173 (valid polygons, faster deskewing, various fixes) by @bertsky in #192

Fixed:

• restoring the contour in the original image caused an error due to an empty tuple, #154
• removed NumPy warnings calculating sigma, mean, (fixed issue #158)
• fixed bug in separate_lines.py, #124
• Drop capitals are now handled separately from their corresponding textline
• Marginals are now divided into left and right. Their reading order is written first for left marginals, then for right marginals, and within each side from top to bottom
• Added a new page extraction model. Instead of bounding boxes, it outputs page contours in the XML file, improving results for skewed pages
• Improved reading order for cases where a textline is segmented into multiple smaller textlines

Changed

• CLIs: read only allowed filename suffixes (image or XML) with --dir_in
• CLIs: make all output option required, and -i / -di required but mutually exclusive
• ocr CLI: drop redundant -brb in favour of just -dib
• APIs: move all input/output path options from class (kwarg and attribute) ro run kwarg
• layout textlines: polygonal also without -cl

Added:

• eynollah machine-based-reading-order CLI to run reading order detection, #175
• eynollah enhancement CLI to run image enhancement, #175
• Improved models for page extraction and reading order detection, #175
• For the lightweight version (layout and textline detection), thresholds are now assigned to the artificial class. Users can apply these thresholds to improve detection of isolated textlines and regions. To counteract the drawback of thresholding, the skeleton of the artificial class is used to keep lines as thin as possible (resolved issues #163 and #161)
• Added and integrated a trained CNN-RNN OCR models
• Added and integrated a trained TrOCR model
• Improved OCR detection to support vertical and curved textlines
• Introduced a new machine-based reading order model with rotation augmentation
• Optimized reading order speed by clustering text regions that belong to the same block, maintaining top-to-bottom order
• Implemented text merging across textlines based on hyphenation when a line ends with a hyphen
• Integrated image enhancement as a separate use case
• Added reading order functionality on the layout level as a separate use case
• CNN-RNN OCR models provide confidence scores for predictions
• Added OCR visualization: predicted OCR can be overlaid on an image of the same size as the input
• Introduced a threshold value for CNN-RNN OCR models, allowing users to filter out low-confidence textline predictions
• For OCR, users can specify a single model by name instead of always using the default model
• Under the OCR use case, if Ground Truth XMLs and images are available, textline image and corresponding text extraction can now be performed

Merged PRs:

• better machine based reading order + layout and textline + ocr by @vahidrezanezhad in #175
• CI: pypi by @kba in #154
• CI: Use most recent actions/setup-python@v5 by @kba in #157
• update docker by @bertsky in #159
• Ocrd fixes by @kba in #167
• Updating readme for eynollah use cases cli by @kba in #166
• OCR-D processor: expose reading_order_machine_based by @bertsky in #171
• prepare release v0.5.0: fix logging by @bertsky in #180
• mb_ro_on_layout: remove copy-pasta code not actually used by @kba in #181
• prepare release v0.5.0: improve CLI docstring, refactor I/O path options from class to run kwargs, increase test coverage @bertsky in #182
• prepare release v0.5.0: fix for OCR doit subtest by @bertsky in #183
• Prepare release v0.5.0 by @kba in #178
• updating eynollah README, how to use it for use cases by @vahidrezanezhad in #156
• add feedback to command line interface by @michalbubula in #170

Fixed:

• allow empty imports for optional dependencies
• avoid Numpy warnings (empty slices etc)
• remove deprecated Numpy types
• binarization CLI: make dir_in usable again

Added:

• Continuous Deployment via Dockerhub and GHCR
• CI: also test CLIs and OCR-D
• CI: measure code coverage, annotate+upload reports
• smoke-test: also check results
• smoke-test: also test sbb-binarize
• ocrd-test: analog for OCR-D CLI (segment and binarize)
• pytest: add asserts, extend coverage, use subtests for various options
• pytest: also add binarization
• pytest: add dir_in mode (segment and binarize)
• make install: control optional dependencies via EXTRAS variable
• OCR-D: expose and describe recently added parameters:
  • ignore_page_extraction
  • allow_enhancement
  • textline_light
  • right_to_left
• OCR-D: 🔥 integrate ocrd-sbb-binarize
• add detection confidence in TextRegion/Coords/@conf
  (but only in light version and not for marginalia)

Changed:

• Docker build: simplify, w/ OCR, conform to OCR-D spec
• OCR-D: 🔥 migrate to core v3
  • initialize+setup only once
  • restrict number of parallel page workers to 1 (conflicts with existing multiprocessing; TF parts not mp-compatible)
  • do query maximally annotated page image
    (but filtering existing binarization/cropping/deskewing),
    rebase (as new @imageFilename) if necessary
  • add behavioural docstring
• 🔥 refactor Eynollah API:
  • no more data (kw)args at init,
    but kwargs dir_in / image_filename for run()
  • no more data attributes, but function kwargs
    (pcgts, image_filename, image_pil, dir_in, override_dpi)
  • remove redundant TF session/model loaders
    (only load once during init)
  • factor run_single() out of run() (loop body),
    expose for independent calls (like OCR-D)
  • expose cache_images(), add dpi kwarg, set self._imgs
  • single-image mode writes PAGE file result
    (just as directory mode does)
• CLI: assertions (instead of print+exit) for options checks
• light mode: fine-tune ratio to better detect a region as header

Fixed:

• regression in OCR-D processor, #106
• Expected Ptrcv::UMat for argument 'contour', #110
• Memory usage explosion with very narrow images (e.g. book spine), #67

Changed:

• Eynollah light integration, #86
• use PEP420 style qurator namespace, #97
• set_memory_growth to all GPU devices alike, #100

Fixed:

• PAGE-XML coordinates can have self-intersections, #20
• reading order representation (XML order vs index), #22
• allow cropping separately, #26
• Order of regions, #51
• error while running inference, #75
• Eynollah crashes while processing image, #77
• ValueError: bad marshal data, #87
• contour extraction: inhomogeneous shape, #92
• Confusing model dir variables, #93
• New release?, #96

Full Changelog: v0.2.0...v0.3.0

Changed:

• Convert default model from HDF5 to TF SavedModel, #91

Added:

• parameter tables to toggle table detection, #91

Fixed:

• Do not produce spurious TextEquiv, #68
• Less spammy logging, #64, #65, #71

Changed:

• Upgrade to tensorflow 2.4.0, #74
• Improved README
• CI: test for python 3.7+, #90

Fixed:

• models parameter should have content-type, #61, OCR-D/core#777