Persian Handwritten Word Recognition by Log-Polar Transform and Hidden Markov Model
Subject Areas : electrical and computer engineeringQ. Nadalinia Charei 1 * , K. Yaghmaie 2 , H. Fazlollahi Aghamalek 3 , S. M. Razavi 4
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Keywords: Log-polar transform hidden Markov model (HMM) Persian handwritten word recognition,
Abstract :
In this paper a recognition system for Persian words is introduced which utilizes the local higher order of the log-polar image autocorrelation for feature extraction of Persian sub-words. This feature extraction technique brings up leads to a system robustness in cases of writing variations alteration like scaled or rotated handwritings. Also using the log-polar transform, the sub-word image sampling will be performed so that most of acquired samples will be centered in a certain area. The proposed method uses the discrete Hidden Markov’s Model (HMM) as a classifier. Furthermore a net of dictionaries were employed to increase the reliability and precision of the system output. Finally, the Iran-Shahr database is utilized to evaluate the system performance. Comparing the results of the proposed method and other previous methods, proves that a less sensitivity has been achieved by the proposed method about handwriting variations.
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