1
Young Researchers and Elite Club, Islamic Azad University Semnan Branch
2
Department of Electrical Engineering, Islamic Azad University Semnan Branch
Abstract
This paper attempts to recognize online Farsi sub-words using the Freeman chain codes and hidden Markov model. Chain codes reduce the number of data with using the direction of breaks and keeping the direction of pen movement. Hence it can be used as an effective way to recognize of online sub-words. After breaking the sub-word into component parts (main body and strokes), each part separately coded using Freeman chain code. Since these codes are not sufficient to recognize of sub-words, we merged them with some other features extracted from horizontal and vertical trajectories. Finally, the set of features are sent hidden Markov model classifier to final recognition. Modeling has been built with Baum-Welch algorithm and training of program performed with forward algorithms. Using the mentioned steps on a database including the 2000 sub-words has the recognition rate of 93.5%.
Ghods, V., & Hosseini, S. (2016). Recognition of Online Farsi Sub-words based on Freeman Chain Code Feature using Hidden Markov Model. Journal of Machine Vision and Image Processing, 3(1), 37-44.
MLA
Vahid Ghods; Sara Hosseini. "Recognition of Online Farsi Sub-words based on Freeman Chain Code Feature using Hidden Markov Model". Journal of Machine Vision and Image Processing, 3, 1, 2016, 37-44.
HARVARD
Ghods, V., Hosseini, S. (2016). 'Recognition of Online Farsi Sub-words based on Freeman Chain Code Feature using Hidden Markov Model', Journal of Machine Vision and Image Processing, 3(1), pp. 37-44.
VANCOUVER
Ghods, V., Hosseini, S. Recognition of Online Farsi Sub-words based on Freeman Chain Code Feature using Hidden Markov Model. Journal of Machine Vision and Image Processing, 2016; 3(1): 37-44.
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