PyPI version


OCR-D wrapper for detectron2 based segmentation models


This offers OCR-D compliant workspace processors for document layout analysis with models trained on Detectron2, which implements Faster R-CNN, Mask R-CNN, Cascade R-CNN, Feature Pyramid Networks and Panoptic Segmentation, among others.

In trying to cover a broad range of third-party models, a few sacrifices have to be made: Deployment of models may be difficult, and needs configuration. Class labels (really PAGE-XML region types) must be provided. The code itself tries to cope with panoptic and instance segmentation models (with or without masks).

Only meant for (coarse) page segmentation into regions – no text lines, no reading order, no orientation.


Create and activate a virtual environment as usual.

To install Python dependencies:

make deps

Which is the equivalent of:

pip install -r requirements.txt -f # for CUDA 11.3

To install this module, then do:

make install

Which is the equivalent of:

pip install .


OCR-D processor interface ocrd-detectron2-segment

To be used with PAGE-XML documents in an OCR-D annotation workflow.

Usage: ocrd-detectron2-segment [OPTIONS]

  Detect regions with Detectron2

  > Use detectron2 to segment each page into regions.

  > Open and deserialize PAGE input files and their respective images.
  > Fetch a raw and a binarized image for the page frame (possibly
  > cropped and deskewed).

  > Feed the raw image into the detectron2 predictor that has been used
  > to load the given model. Then, depending on the model capabilities
  > (whether it can do panoptic segmentation or only instance
  > segmentation, whether the latter can do masks or only bounding
  > boxes), post-process the predictions:

  > - panoptic segmentation: take the provided segment label map, and
  >   apply the segment to class label map
  > - instance segmentation: find an optimal non-overlapping set (flat
  >   map) of instances via non-maximum suppression; then extend / shrink
  >   the surviving masks to fully include / exclude connected components
  >   in the foreground that are on the boundary

  > Finally, find the convex hull polygon for each region, and map its
  > class id to a new PAGE region type (and subtype).

  > Produce a new output file by serialising the resulting hierarchy.

  -I, --input-file-grp USE        File group(s) used as input
  -O, --output-file-grp USE       File group(s) used as output
  -g, --page-id ID                Physical page ID(s) to process
  --overwrite                     Remove existing output pages/images
                                  (with --page-id, remove only those)
  -p, --parameter JSON-PATH       Parameters, either verbatim JSON string
                                  or JSON file path
  -P, --param-override KEY VAL    Override a single JSON object key-value pair,
                                  taking precedence over --parameter
  -m, --mets URL-PATH             URL or file path of METS to process
  -w, --working-dir PATH          Working directory of local workspace
                                  Log level
  -C, --show-resource RESNAME     Dump the content of processor resource RESNAME
  -L, --list-resources            List names of processor resources
  -J, --dump-json                 Dump tool description as JSON and exit
  -h, --help                      This help message
  -V, --version                   Show version

   "categories" [array - REQUIRED]
    maps each region category (position) of the model to a PAGE region
    type (and subtype if separated by colon), e.g.
    ['TextRegion:paragraph', 'TextRegion:heading',
    'TextRegion:floating', 'TableRegion', 'ImageRegion'] for PubLayNet
   "min_confidence" [number - 0.5]
    confidence threshold for detections
   "model_config" [string - REQUIRED]
    path name of model config
   "model_weights" [string - REQUIRED]
    path name of model weights
   "device" [string - "cuda"]
    select computing device for Torch (e.g. cpu or cuda:0); will fall
    back to CPU if no GPU is available


ocrd resmgr download -n ocrd-detectron2-segment
ocrd resmgr download -n ocrd-detectron2-segment
ocrd-detectron2-segment -I OCR-D-BIN -O OCR-D-SEG-TAB -P categories '["TableRegion"]' -P model_config All_X152.yaml -P model_weights model_final.pth -P min_confidence 0.1


Note: These are just examples, no exhaustive search was done yet!

Note: Make sure you unpack first if the download link is an archive. Also, the filename suffix (.pth vs .pkl) of the weight file does matter!


R152-FPN config|weights|["TableRegion"]


R50-FPN config|weights|["TextRegion:paragraph", "TextRegion:heading", "TextRegion:list-label", "TableRegion", "ImageRegion"]

R101-FPN config|weights|["TextRegion:paragraph", "TextRegion:heading", "TextRegion:list-label", "TableRegion", "ImageRegion"]

X101-FPN config|weights|["TextRegion:paragraph", "TextRegion:heading", "TextRegion:list-label", "TableRegion", "ImageRegion"]


R50-FPN config|weights|["TextRegion:paragraph", "TextRegion:heading", "TextRegion:list-label", "TableRegion", "ImageRegion"]

R101-FPN config|weights|["TextRegion:paragraph", "TextRegion:heading", "TextRegion:list-label", "TableRegion", "ImageRegion"]


provides different model variants of various depths for multiple datasets:

See here for an overview. You will have to adapt the label map to conform to PAGE-XML region (sub)types accordingly.


X101-FPN archive

Proposed mappings:

  • ["TextRegion:heading", "TextRegion:credit", "TextRegion:caption", "TextRegion:other", "MathsRegion", "GraphicRegion", "TextRegion:footer", "TextRegion:floating", "TextRegion:paragraph", "TextRegion:endnote", "TextRegion:heading", "TableRegion", "TextRegion:heading" (using only predefined @type)
  • ["TextRegion:abstract", "TextRegion:author", "TextRegion:caption", "TextRegion:date", "MathsRegion", "GraphicRegion", "TextRegion:footer", "TextRegion:list", "TextRegion:paragraph", "TextRegion:reference", "TextRegion:heading", "TableRegion", "TextRegion:title" (using @custom as well)


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