HIGH ACCURACY RECOGNITION ALGORITHM OF ARABIC CHARACTERS

This paper presents a fast high accuracy recognition algorithm for typewritten Arabic characters. The procedure of recognition consists of two parallel stages. The first one is devoted to recognize the main body of the charcacter where the outer contour of the character main body is obtained and a limited number of Fourier descriptors are derived from the resulting contour. The second stage utilizes the topological features to classify the stress mark(s) if any exist, the extraction of topological features depends on a new technique called Pixels' Numbers and Position (PNP) of stress marks. Combining these two interrelated stages gives high recognition results while maintaining computational simplicity

Feature Extraction Methods for Character Recognition

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ISSUES AND PROBLEMS IN THE RECOGNITION OF ARABIC PRINTED TEXTS

Nowadays, Arabic text recognition bears witness to a wave of interest after a long period of moderate activity. The reason is the complexity of the problem manifested in both cursive shapes and close similarity of Arabic characters. Optical character recognition this is performed usually by detecting and quantifying isolated characters, which implies that the text is meaningfully segmented into more simple shapes. In this paper we study the properties of the Arabic script and review the problems encountered in its segmentation. To pass by the need for segmentation a new technique, the so-called N-markers, is proposed. It unifies the advantages of both global and structural recognition methods and is intuitively close to the human recognition process. The technique is tailored to single-font printed texts rich in ligatures, a problem encountered in good quality books and journals. It can be extended, in a straightforward way, to other fonts and also to handle degraded texts. Preliminary experiments show encouraging results

Arabic Handwritten Alphanumeric Character Recognition using Fuzzy Attributed Turning Functions

In this paper, we present a novel method for recognition of unconstrained handwritten Arabic alphanumeric characters. The algorithm binarizes the character image, smoothes it and extracts its contour. A novel approach for polygonal approximation of handwritten character contours is applied. The directions and length features are extracted from the polygonal approximation. These features are used to build character models in the training phase. For the recognition purpose, we introduce Fuzzy Attributed Turning Functions (FATF) and define a dissimilarity measure based on FATF for comparing polygonal shapes. Experimental results demonstrate the effectiveness of our algorithm for recognition of handwritten Arabic characters. We have obtained around 98% accuracy for Arabic handwritten characters and more than 97% accuracy for handwritten Arabic numerals