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45 lines
1.9 KiB
Markdown
45 lines
1.9 KiB
Markdown
# OCR - Optical Character Recognition
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This software implements a heavily parallelized pipeline to recognize text in PDF files. It is used for nopaque's OCR service but you can also use it standalone, for that purpose a convenient wrapper script is provided.
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## Software used in this pipeline implementation
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- Official Debian Docker image (buster-slim): https://hub.docker.com/_/debian
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- Software from Debian Buster's free repositories
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- ocropy (1.3.3): https://github.com/ocropus/ocropy/releases/tag/v1.3.3
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- pyFlow (1.1.20): https://github.com/Illumina/pyflow/releases/tag/v1.1.20
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- Tesseract OCR (4.1.1): https://github.com/tesseract-ocr/tesseract/releases/tag/4.1.1
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- tessdata_best (4.1.0): https://github.com/tesseract-ocr/tessdata_best/releases/tag/4.1.0
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## Use this image
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1. Create input and output directories for the pipeline.
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``` bash
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mkdir -p /<my_data_location>/input /<my_data_location>/output
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```
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2. Place your PDF files inside `/<my_data_location>/input`. Files should all contain text of the same language.
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3. Start the pipeline process. Check the pipeline help (`ocr --help`) for more details.
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```
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# Option one: Use the wrapper script
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## Install the wrapper script (only on first run). Get it from https://gitlab.ub.uni-bielefeld.de/sfb1288inf/ocr/-/raw/development/wrapper/ocr, make it executeable and add it to your ${PATH}
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cd /<my_data_location>
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ocr -i input -l <language_code> -o output <optional_pipeline_arguments>
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# Option two: Classic Docker style
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docker run \
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--rm \
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-it \
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-u $(id -u $USER):$(id -g $USER) \
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-v /<my_data_location>/input:/input \
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-v /<my_data_location>/output:/output \
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gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/ocr:development \
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-i /ocr_pipeline/input \
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-l <language_code> \
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-o /ocr_pipeline/output \
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<optional_pipeline_arguments>
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```
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4. Check your results in the `/<my_data_location>/output` directory.
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