Arabic Handwritten Word Recognition Using HMMs with Explicit State Duration
نویسندگان
چکیده
منابع مشابه
Arabic Handwritten Word Recognition Using HMMs with Explicit State Duration
We describe an offline unconstrained Arabic handwritten word recognition system based on segmentation-free approach and discrete hidden Markov models (HMMs) with explicit state duration. Character durations play a significant part in the recognition of cursive handwriting. The duration information is still mostly disregarded in HMM-based automatic cursive handwriting recognizers due to the fact...
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The goal of this paper is to describe an off-line segmentation-free Arabic handwritten words recognition system. This system is based on a semi-continuous 1dimensionnal hidden Markov models (SCHMMs) with explicit state duration of different kinds (Gauss, Poisson and Gamma). First preprocessing is applied to simplify the feature extraction process, then the word image is analyzed from right-to-l...
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Hidden Markov Models (HMMs) are now widely used for off-line text recognition in many languages and, in particular, Arabic. In previous work, we proposed to directly use columns of raw, binary image pixels, which are directly fed into embedded Bernoulli (mixture) HMMs, that is, embedded HMMs in which the emission probabilities are modeled with Bernoulli mixtures. The idea was to by-pass feature...
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ژورنال
عنوان ژورنال: EURASIP Journal on Advances in Signal Processing
سال: 2007
ISSN: 1687-6180
DOI: 10.1155/2008/247354