Deep learning OCR post-correction
Evaluation and post-correction of OCR of digitised historical newspapers
ochre
A tool to clean up text generated by OCR using individual words as well as their context.
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mentions
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contributor
What ochre can do for you
- Train character-based language models/LSTMs for OCR post-correction
- Ready to use workflows for data preprocessing, training correction models, doing the post-correction, and analyzing (remaining) errors
- Compare (corrected) OCR text to the gold standard based on character error rate (CER), word error rate (WER), and order independent word error rate
- Analyze OCR errors on the word level
- Discover OCR post-correction data sets
Ochre is experimental software for cleaning up text with OCR mistakes. The software was developed to investigate whether character-based language models can be used to remove OCR mistakes. In addition, ochre provides functionality to analyze the kinds of OCR mistakes in a corpus. This enables researchers to compare different OCR post-correction methods and find out what kinds of mistakes they are good at solving.
Tags
Programming Language
- Python
License
- Apache-2.0
Source code
Participating organizations
Projects with ochre
Contributors
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Janneke van der ZwaanNetherlands eScience Center
Contact person
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