<title>Printed Arabic document recognition system</title>

Abstract

As a cursive script, the characteristics of Arabic texts are different from Latin or Chinese greatly. For example, an Arabic character has up to four written forms and characters that can be joined are always joined on the baseline. Therefore, the methods used for Arabic document recognition are special, where character segmentation is the most critical problem. In this paper, a printed Arabic document recognition system is presented, which is composed of text line segmentation, word segmentation, character segmentation, character recognition and post-processing stages. In the beginning, a top-down and bottom-up hybrid method based on connected components classification is proposed to segment Arabic texts into lines and words. Subsequently, characters are segmented by analysis the word contour. At first the baseline position of a given word is estimated, and then a function denote the distance between contour and baseline is analyzed to find out all candidate segmentation points, at last structure rules are proposed to merge over-segmented characters. After character segmentation, both statistical features and structure features are used to do character recognition. Finally, lexicon is used to improve recognition results. Experiment shows that the recognition accuracy of the system has achieved 97.62%.