# 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. The pipeline is designed to run on Linux operating systems, but with some tweaks it should also run on Windows with WSL installed.

## 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 (5.0.0): https://github.com/tesseract-ocr/tesseract/releases/tag/5.0.0

## Installation

1. Install Docker and Python 3.
2. Clone this repository: `git clone https://gitlab.ub.uni-bielefeld.de/sfb1288inf/ocr.git`
2. Build the Docker image: `docker build -t gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/ocr:0.1.0 ocr`
2. Add the wrapper script (`wrapper/ocr` relative to this README file) to your `${PATH}`.
3. Create working directories for the pipeline: `mkdir -p /<my_data_location>/{input,models,output}`.
4. Place your Tesseract OCR model(s) inside `/<my_data_location>/models`.

## Use the Pipeline

1. Place your PDF files inside `/<my_data_location>/input`. Files should all contain text of the same language.
2. Clear your `/<my_data_location>/output` directory.
3. Start the pipeline process. Check the pipeline help (`ocr --help`) for more details.
```bash
cd /<my_data_location>
ocr -i input -o output -m models/<model_name> -l <language_code> <optional_pipeline_arguments>
# or
ocr -i input -o output -m models/* -l <language_code> <optional_pipeline_arguments>
```
4. Check your results in the `/<my_data_location>/output` directory.