Using Dynamic Time Warping for Persian Handwriting Recognition
نویسندگان
چکیده
This paper discusses the use of fast and customized Dynamic Time Warping method for offline Persian handwriting recognition that could be easily extended to Arabic language. Many systems in this field are based on either Neural Network or Hidden Markov Model that suffer from low recognition rate, sensitivity to noises or wide range of parameters that reduce system performance. The complete system is able to recognize Persian handwritten words of 380 individual writers and the experimental results conducted on the benchmark IAUT/PHCN database of Iranian handwritten city names show the proposed algorithm has the recognition rate above 90%.
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