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Dockerfile | ||
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ocr | ||
README.md |
OCR - Optical Character Recognition
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.
Software used in this pipeline implementation
- Official Debian Docker image (buster-slim): https://hub.docker.com/_/debian
- Software from Debian Buster's free repositories
- ocropy (1.3.3): https://github.com/ocropus/ocropy/releases/tag/v1.3.3
- pyFlow (1.1.20): https://github.com/Illumina/pyflow/releases/tag/v1.1.20
- Tesseract OCR (4.1.1): https://github.com/tesseract-ocr/tesseract/releases/tag/4.1.1
- tessdata_best (4.1.0): https://github.com/tesseract-ocr/tessdata_best/releases/tag/4.1.0
Use this image
- Create input and output directories for the pipeline.
mkdir -p /<my_data_location>/input /<my_data_location>/output
-
Place your PDF files inside
/<my_data_location>/input
. Files should all contain text of the same language. -
Start the pipeline process. Check the Pipeline arguments section for more details.
# Option one: Use the wrapper script
## 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}
cd /<my_data_location>
ocr -i input -l <language_code> -o output <optional_pipeline_arguments>
# Option two: Classic Docker style
docker run \
--rm \
-it \
-u $(id -u $USER):$(id -g $USER) \
-v /<my_data_location>/input:/input \
-v /<my_data_location>/output:/output \
gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/ocr:development \
-i /ocr_pipeline/input \
-l <language_code> \
-o /ocr_pipeline/output \
<optional_pipeline_arguments>
- Check your results in the
/<my_data_location>/output
directory.
Pipeline arguments
Mandatory arguments
-i, --input-dir INPUT_DIR
- Input directory
-o, --output-dir OUTPUT_DIR
- Output directory
-l, --language {spa,fra,dan,deu,eng,frm,chi_tra,ara,enm,ita,ell,frk,rus,por}
- Language of the input (3-character ISO 639-2 language codes)
Optional arguments
--binarize
- Add binarization as a preprocessing step
--log-dir
- Logging directory
--mem-mb
- Amount of system memory to be used (Default: min(--n-cores * 2048, available system memory))
--n-cores
- Number of CPU threads to be used (Default: min(4, available CPU cores))
-v, --version
- Returns the current version of the OCR pipeline
# Example with all arguments used
docker run \
--rm \
-it \
-u $(id -u $USER):$(id -g $USER) \
-v /<my_data_location>/input:/ocr_pipeline/input \
-v /<my_data_location>/output:/ocr_pipeline/output \
-v /<my_data_location>/logs:/ocr_pipeline/logs \
gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/ocr:development \
-i /ocr_pipeline/input \
-l eng \
-o /ocr_pipeline/output \
--binarize \
--log-dir /ocr_pipeline/logs \
--n-cores 8 \