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Online Arabic handwriting recognition using continuous Gaussian mixture HMMS
Al-Habian G.,
Published in IEEE
Pages: 1183 - 1186
In this paper, we present a recognizer structure aimed at recognizing online Arabic handwriting written in continuous form. The basic units of recognition used are strokes, which are sub-letter parts. To recognize strokes we used Hidden Markov Models (HMMs) to model each stroke. Decision logic was then used to interpret the output of stroke HMMs, converting their output into recognizedwords. Data collected from six writers was used to validate the functionality of the system. Experimental simulation of the proposed system resulted in promising recognition rates (>75%), which is significantly better than currently available solutions. ©2007 IEEE.
About the journal
JournalData powered by Typeset2007 International Conference on Intelligent and Advanced Systems
PublisherData powered by TypesetIEEE
Open AccessNo