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85 lines
2.9 KiB
Markdown
85 lines
2.9 KiB
Markdown
# ocrd_calamari
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> Recognize text using [Calamari OCR](https://github.com/Calamari-OCR/calamari).
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[](https://circleci.com/gh/OCR-D/ocrd_calamari)
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[](https://pypi.org/project/ocrd_calamari/)
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[](https://codecov.io/gh/OCR-D/ocrd_calamari)
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## Introduction
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**ocrd_calamari** offers a [OCR-D](https://ocr-d.de) compliant workspace processor for the functionality of Calamari OCR. It uses OCR-D workspaces (METS) with [PAGE XML](https://github.com/PRImA-Research-Lab/PAGE-XML) documents as input and output.
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This processor only operates on the text line level and so needs a line segmentation (and by extension a binarized
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image) as its input.
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In addition to the line text it may also output word and glyph segmentation
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including per-glyph confidence values and per-glyph alternative predictions as
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provided by the Calamari OCR engine, using a `textequiv_level` of `word` or
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`glyph`. Note that while Calamari does not provide word segmentation, this
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processor produces word segmentation inferred from text
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segmentation and the glyph positions. The provided glyph and word segmentation
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can be used for text extraction and highlighting, but is probably not useful for
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further image-based processing.
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## Installation
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### From PyPI
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```
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pip install ocrd_calamari
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```
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### From Repo
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```sh
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pip install .
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```
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## Install models
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Download models trained on GT4HistOCR data:
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```
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make gt4histocr-calamari1
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ls gt4histocr-calamari1
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```
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Manual download: [model.tar.xz](https://qurator-data.de/calamari-models/GT4HistOCR/2019-12-11T11_10+0100/model.tar.xz)
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## Example Usage
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Before using `ocrd-calamari-recognize` get some example data and model, and
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prepare the document for OCR:
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```
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# Download model and example data
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make gt4histocr-calamari1
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make actevedef_718448162
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# Create binarized images and line segmentation using other OCR-D projects
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cd actevedef_718448162
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ocrd-olena-binarize -P impl sauvola-ms-split -I OCR-D-IMG -O OCR-D-IMG-BIN
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ocrd-tesserocr-segment-region -I OCR-D-IMG-BIN -O OCR-D-SEG-REGION
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ocrd-tesserocr-segment-line -I OCR-D-SEG-REGION -O OCR-D-SEG-LINE
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```
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Finally recognize the text using ocrd_calamari and the downloaded model:
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```
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ocrd-calamari-recognize -P checkpoint "../gt4histocr-calamari1/*.ckpt.json" -I OCR-D-SEG-LINE -O OCR-D-OCR-CALAMARI
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```
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or
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```
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ocrd-calamari-recognize -P checkpoint_dir "../gt4histocr-calamari1" -I OCR-D-SEG-LINE -O OCR-D-OCR-CALAMARI
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```
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You may want to have a look at the [ocrd-tool.json](ocrd_calamari/ocrd-tool.json) descriptions
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for additional parameters and default values.
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## Development & Testing
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For information regarding development and testing, please see
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[README-DEV.md](README-DEV.md).
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