A generative probabilistic OCR model for NLP applications
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Development in parsing technology: from theory to application
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OCR post-processing for low density languages
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Topic based language models for OCR correction
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A General Method for Transforming Standard Parsers into Error-Repair Parsers
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Rejection threshold estimation for an unknown language model in an OCR task
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ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
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In this paper, stochastic error-correcting parsing is proposed as a powerful and flexible method to post-process the results of an optical character recognizer (OCR). Deterministic and non-deterministic approaches are possible under the proposed setting. The basic units of the model can be words or complete sentences, and the lexicons or the language databases can be simple enumerations or may convey probabilistic information from the application domain.