Abstract
In this paper we present a hybrid language model for the recognition of handwritten historical documents with a structured syntactical layout. Using a hidden Markov model-based recognition framework, a word-based grammar with a closed dictionary is enhanced by a character sequence recognition method. This allows to recognize out-of-dictionary words in controlled parts of the recognition, while keeping a closed vocabulary restriction for other parts. While the current status is work in progress, we can report an improvement in terms of character error rate.
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Cirera, N., Fornés, A., Frinken, V., Lladós, J. (2013). Hybrid Grammar Language Model for Handwritten Historical Documents Recognition. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_13
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DOI: https://doi.org/10.1007/978-3-642-38628-2_13
Publisher Name: Springer, Berlin, Heidelberg
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