# 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

1. Create input and output directories for the pipeline.
``` bash
mkdir -p /<my_data_location>/input /<my_data_location>/output
```

2. Place your PDF files inside `/<my_data_location>/input`. Files should all contain text of the same language.

3. Start the pipeline process. Check the pipeline help (`ocr --help`) 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>
```

4. Check your results in the `/<my_data_location>/output` directory.