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Improving Book OCR by Adaptive Language and Image Models

Dar-Shyang Lee
Proceedings of 2012 10th IAPR International Workshop on Document Analysis Systems, IEEE, pp. 115-119


In order to cope with the vast diversity of book content and typefaces, it is important for OCR systems to leverage the strong consistency within a book but adapt to variations across books. In this work, we describe a system that combines two parallel correction paths using document-specific image and language models. Each model adapts to shapes and vocabularies within a book to identify inconsistencies as correction hypotheses, but relies on the other for effective cross-validation. Using the open source Tesseract engine as baseline, results on a large dataset of scanned books demonstrate that word error rates can be reduced by 25% using this approach.

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