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34 lines
1.8 KiB
Markdown
34 lines
1.8 KiB
Markdown
# OCR - Optical Character Recognition
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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.
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## Software used in this pipeline implementation
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- Official Debian Docker image (buster-slim): https://hub.docker.com/_/debian
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- Software from Debian Buster's free repositories
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- ocropy (1.3.3): https://github.com/ocropus/ocropy/releases/tag/v1.3.3
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- pyFlow (1.1.20): https://github.com/Illumina/pyflow/releases/tag/v1.1.20
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- Tesseract OCR (5.0.0): https://github.com/tesseract-ocr/tesseract/releases/tag/5.0.0
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## Installation
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1. Install Docker and Python 3.
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2. Clone this repository: `git clone https://gitlab.ub.uni-bielefeld.de/sfb1288inf/ocr.git`
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2. Build the Docker image: `docker build -t gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/ocr:0.1.0 ocr`
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2. Add the wrapper script (`wrapper/ocr` relative to this README file) to your `${PATH}`.
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3. Create working directories for the pipeline: `mkdir -p /<my_data_location>/{input,models,output}`.
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4. Place your Tesseract OCR model(s) inside `/<my_data_location>/models`.
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## Use the Pipeline
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1. Place your PDF files inside `/<my_data_location>/input`. Files should all contain text of the same language.
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2. Clear your `/<my_data_location>/output` directory.
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3. Start the pipeline process. Check the pipeline help (`ocr --help`) for more details.
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```bash
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cd /<my_data_location>
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ocr -i input -o output -m models/<model_name> -l <language_code> <optional_pipeline_arguments>
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# or
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ocr -i input -o output -m models/* -l <language_code> <optional_pipeline_arguments>
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```
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4. Check your results in the `/<my_data_location>/output` directory.
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