# 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 arguments](#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>
```

4. 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

``` bash
# 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 \
```