An Adaptive Algorithm for the Automatic Segmentation of Printed Arabic Text

Character segmentation is a crucial step in most Arabic optical text recognition systems. The recognition process depends mainly on the accuracy of the character segmentation. This paper presents a novel adaptive algorithm for the off-line segmentation of printed Arabic text. There are many challenging features in the Arabic writing, for example, it is cursive and characters in a word can take four different shapes. A general rule cannot apply for segmenting all the characters. Hence, we propose an adaptive rule-based segmentation algorithm based on the general structural relationship of the Arabic text. The main rule used is that “most characters start with and end before a T-junction on the baseline.” Some other rules are included to take care of the variations in the main rule. Results show the efficiency of the proposed algorithm, where it is found to achieve a segmentation accuracy of 96.5%.