This repository aims to provide a number of OCR-D compliant processors for layout analysis and evaluation.

In your Python virtual environment, run:

pip install ocrd_segment

Contains processors for various tasks:

• exporting segment images (including results from preprocessing like cropping/masking, deskewing, dewarping or binarization) along with polygon coordinates and metadata:
  • ocrd-segment-extract-pages (for pages, also exports MS-COCO format and pageview plots)
  • ocrd-segment-extract-regions (for regions, so exports MS-COCO format))
  • ocrd-segment-extract-lines (for lines, also exports text and .xlsx)
  • ocrd-segment-extract-words (for words, also exports text)
  • ocrd-segment-extract-glyphs (for glyphs, also exports text)
• importing layout segmentations from other formats:
  • ocrd-segment-from-masks (for mask/label images, i.e. semantic segmentation)
  • ocrd-segment-from-coco (for MS-COCO annotation)
• post-processing or repairing layout segmentations:
  • ocrd-segment-repair (validity and consistency of all coordinates; also, for regions, reduce overlaps/redundancy between neighbours, and/or simplify polygons, and/or shrink to the alpha shape of foreground contours)
  • ocrd-segment-project (remake segment coordinates into the concave hull / alpha shape of their constituents)
  • ocrd-segment-replace-original (rebase all segments on cropped deskewed border frame as new full page)
  • ocrd-segment-replace-page (2 input fileGrps; overwrite segmentation below page of first fileGrp by all segments of second fileGrp, rebasing all coordinates; "inverse" of replace-original)
  • ocrd-segment-replace-text (insert text below page from single-segment text files; "inverse" of extract-*)
• comparing different layout segmentations:
  • ocrd-segment-evaluate 🚧 (2 input fileGrps; align, compare and evaluate page segmentations; early stage)
  • page-segment-evaluate (same with standalone CLI)
• pattern-based segmentation (input file groups N=1, based on a PAGE template, e.g. from Aletheia, and some XSLT or Python to apply it to the input file group)
  • ocrd-segment-via-template 🚧 (unpublished)
• data-driven segmentation (input file groups N=1, based on a statistical model, e.g. Neural Network)
  • ocrd-segment-via-model 🚧 (unpublished)

For detailed behaviour, see --help on each processor CLI. For detailed description on input/output and parameters, see ocrd-tool.json or --dump-json on each processor CLI.

Requires libgeos-dev library for building shapely binary requirement, see Shapely Installation from source. Please ensure it's available before trying to install local requirements.

None yet.