Page segmentation and segmentation evaluation
Project description
ocrd_segment
This repository aims to provide a number of OCR-D-compliant processors for layout analysis and evaluation.
Installation
In your virtual environment, run:
pip install .
Usage
- exporting page images (including results from preprocessing like cropping/masking, deskewing, dewarping or binarization) along with region polygon coordinates and metadata, also MS-COCO:
- exporting region images (including results from preprocessing like cropping/masking, deskewing, dewarping or binarization) along with region polygon coordinates and metadata:
- exporting line images (including results from preprocessing like cropping/masking, deskewing, dewarping or binarization) along with line polygon coordinates and metadata:
- importing layout segmentations from other formats (mask images, MS-COCO JSON annotation):
- repairing layout segmentations (input file groups N >= 1, based on heuristics implemented using Shapely):
- ocrd-segment-repair :construction: (much to be done)
- comparing different layout segmentations (input file groups N = 2, compute the distance between two segmentations, e.g. automatic vs. manual):
- ocrd-segment-evaluate :construction: (very early stage)
- 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
:construction: (unpublished)
- data-driven segmentation (input file groups N=1, based on a statistical model, e.g. Neural Network)
ocrd-segment-via-model
:construction: (unpublished)
For detailed description on input/output and parameters, see ocrd-tool.json
Testing
None yet.
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