Abstract
This paper presents a concept of bidirectional probabilistic character language model and its application to handwriting recognition. Character language model describes probability distribution of adjacent character combinations in words. Bidirectional model applies word analysis from left to right and in reversed order, i.e. it uses conditional probabilities of character succession and character precedence. Character model is used for HMM creation, which is applied as a soft word classifier. Two HMMs are created for left-to-right and right-to-left analysis. Final word classification is obtained as a combination of unidirectional recognitions. Experiments carried out with medical texts recognition revealed the superiority of combined classifier over its components.
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© 2006 Springer-Verlag Berlin Heidelberg
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Sas, J. (2006). Application of Bidirectional Probabilistic Character Language Model in Handwritten Words Recognition. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_82
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DOI: https://doi.org/10.1007/11875581_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
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