ocr/README.md
2022-01-27 13:40:23 +01:00

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# 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`
3. Build the Docker image: `docker build -t gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/ocr:v0.1.0 ocr`
4. Add the wrapper script (`wrapper/ocr` relative to this README file) to your `${PATH}`.
5. Create working directories for the pipeline: `mkdir -p /<my_data_location>/{input,models,output}`.
6. 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>
# <model_code> is the model filename without the ".traineddata" suffix
ocr \
--input-dir input \
--output-dir output \
--model-file models/<model>
-m <model_code> <optional_pipeline_arguments>
# More then one model
ocr \
--input-dir input \
--output-dir output \
--model-file models/<model1>
--model-file models/<model2>
-m <model1_code>+<model2_code> <optional_pipeline_arguments>
# Instead of multiple --model-file statements, you can also use
ocr \
--input-dir input \
--output-dir output \
--model-file models/*
-m <model1_code>+<model2_code> <optional_pipeline_arguments>
```
4. Check your results in the `/<my_data_location>/output` directory.