Using Heuristics Based Approach for Segmentation and Recognition of Printed Arabic Characters

In this study, we propose a flexible template-matching algorithm for word segmentation, and structural analysis of features extraction is used for character recognition in the printed Arabic text. The input text image is preprocessed by the binarization and then by morphological operations. A vector quantization of the thinned image (VQTM) is created based on the idea of a freeman chain code tracking method. In the segmentation process, 113 character templates are compared for partially/completely existence in the VQTM. A nonlinear filter is applied on the segmented regions to extract the termination and bifurcation features. The spatial distribution of the extracted features and other statistical characteristics are analyzed for the verification of recognition. Experimental results show that the overall recognition rate of the three fonts: Arabic transparent, simplified Arabic and traditional Arabic is 98.63%.