Pixel distribution-based features for offline Arabic handwritten word recognition
Qais Ali Al Nuzaili, Siti Zaiton Mohd Hashim, Faisal Saeed, Mohammed Sayim Khalil, Dzulkifli Mohamad · International Journal of Computational Vision and Robotics · 2017
Handwritten word recognition is the ability of a computer to receive and interpret intelligible handwritten input. An important document recognition application is bank cheque processing. The Arabic bank cheque processing system has not been studied as much as Latin and Chinese systems. The domain of handwriting in the Arabic script presents unique technical challenges; proposing a model for feature extraction which combines multiple types of features most likely will help to improve the recognition rate. This work proposed a pixel distribution-based features model (PDM) for offline Arabic handwritten word recognition. Two combination levels were used: the first combines different features and the second combination was done by ensemble classifiers. The AHDB dataset was used, and the experimental results showed superior performance when combining multiple features and using multi classifiers.