Intelligent system for Arabic character recognition
Majida Albakoor, Khalid Saeed, Fadel Sukkar · 2009
In this work, a new system for Arabic letter recognition is designed and implemented. New approaches for segmentation, processing, classification and hence recognition of characters and scripts are shown. The research concentrates on two important subjects: First, segmentation on the basis of word histogram and baseline estimation - a convenient algorithm is worked out for this aim. Second, the process of feature extraction to find the most useful points is implemented upon the given algorithm. Feature coding is executed as a string of eight digits through two counterclockwise passes. The code is filtered up provided with eight basic pairs. The filtered code goes through processing to form an array of 9*9 elements, in addition to an array of 2*2 elements determined to resemble the four parts of the extracted character image. The 85 obtained elements are the input to a Backpropagation Neural Network used for classification purposes. A 98.7% rate of recognition is achieved for Arabic character classification. Results have proved high recognition of Arabic letters for varieties of fonts and sizes. They have also assured that computing time is negligible with very small errors.