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. 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 data set of scanned books demonstrate that word error rates can be reduced by 25 percent using this approach.
展开▼
