Recognition of online Farsi handwriting based on freeman chain code using hidden Markov model
Vahid Ghods, Mohammad Karim Sohrabi, Sara Hosseini · 2016
This paper attempts to recognize online Farsi handwriting 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 recognition of online sub-words. After breaking the sub-word into component parts (main body and strokes), each part separately codes using freeman chain code. Since these codes are not sufficient to recognize of sub-words, they were merged with some other features extracted from horizontal and vertical trajectories. Finally, the set of features was classified by hidden Markov model. Modeling has built with Baum-Welch algorithm and training of the samples performed with forward algorithms. Using the mentioned steps on a database including the 2000 sub-words has the recognition rate of %93.5.