N-gram language models for document image decoding

Gary E. Kopec; Maya R. Said; Kris Popat

doi:10.1117/12.450728

18 December 2001 N-gram language models for document image decoding

Gary E. Kopec, Maya R. Said, Kris Popat

Proceedings Volume 4670, Document Recognition and Retrieval IX; (2001) https://doi.org/10.1117/12.450728
Event: Electronic Imaging, 2002, San Jose, California, United States

Abstract

This paper explores the problem of incorporating linguistic constraints into document image decoding, a communication theory approach to document recognition. Probabilistic character n-grams (n=2--5) are used in a two-pass strategy where the decoder first uses a very weak language model to generate a lattice of candidate output strings. These are then re-scored in the second pass using the full language model. Experimental results based on both synthesized and scanned data show that this approach is capable of improving the error rate by a factor of two to ten depending on the quality of the data and the details of the language model used.

Citation Download Citation

Gary E. Kopec, Maya R. Said, and Kris Popat "N-gram language models for document image decoding", Proc. SPIE 4670, Document Recognition and Retrieval IX, (18 December 2001); https://doi.org/10.1117/12.450728

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