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Published in:
2009 | OriginalPaper | Chapter
Bernoulli HMMs at Subword Level for Handwritten Word Recognition
Authors : Adrià Giménez, Alfons Juan
Published in: Pattern Recognition and Image Analysis
Publisher: Springer Berlin Heidelberg
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This paper presents a handwritten word recogniser based on HMMs at subword level (characters) in which state-emission probabilities are governed by multivariate Bernoulli probability functions. This recogniser works directly with raw binary pixels of the image, instead of conventional, real-valued local features. A detailed experimentation has been carried out by varying the number of states, and comparing the results with those from a conventional system based on continuous (Gaussian) densities. From this experimentation, it becomes clear that the proposed recogniser is much better than the conventional system.