Bounding the probability of error for high precision optical character recognition
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Why multiple document image binarizations improve OCR
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
Multilingual OCR research and applications: an overview
Proceedings of the 4th International Workshop on Multilingual OCR
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Despite ubiquitous claims that optical character recognition (OCR) is a "solved problem,'' many categories of documents continue to break modern OCR software such as documents with moderate degradation or unusual fonts.Many approaches rely on pre-computed or stored character models, but these are vulnerable to cases when the font of a particular document was not part of the training set, or when there is so much noise in a document that the font model becomes weak.To address these difficult cases, we present a form of iterative contextual modeling that learns character models directly from the document it is trying to recognize.We use these learned models both to segment the characters and to recognize them in an incremental, iterative process. We present results comparable to those of a commercial OCR system on a subset of characters from a difficult test document.