Recognition of Tifinaghe Characters Using a Multilayer Neural Network

Abstract In this paper, we present an off line Tifinaghe characters recognition system. Texts are scanned using a flatbed scanner. Digitized text are normalised, noise is reduced using a median filter, baseline skew is corrected by the use of the Hough transform, and text is segmented into line and lines into words. Features are extracted using the Walsh Transformation. Finally characters are recognized by a multilayer neural network. Keywords: Tifinaghe Characters, Baseline Skew Correction, Segmentation, Walsh Transform, Hough Transform, Neural Network, Recognition. 1. INTRODUCTION Optical Character Recognition (OCR) is one of the most successful applications of automatic pattern recognition. It is a very active field of research and development. Several studies have been conducted on Latin, Arabic and Chinese characters [1, 2, 3, 4, 5, 6, 7, 8, 9 ]. However, for Tifinaghe characters system few works was done [13, 14, 15, 16]. Succession of operations in most digital image recognition system can be divided into three stages. First stage is a pre-processing including thresholding improving image quality, segmentation and son on. Second, features extraction for avoiding data abundance and reducing its dimension. Third stage is a classification. During this stage classes name is joint with unknown image by extracted features analyses and matching its representatives of the class, which the classifier has trained at a stage of training. In this study a recognition system (Figure 1) for the recognition of Tifinaghe characters issued from an image scanner is presented. Initially, an image that contains Tifinaghe characters is normalized and segmented to produce a data base. Then, we applied the approach of Walsh Transform to extracted features which are used in the classification phase with a multilayer neural network. The organisation of this paper is as follows. In section 2 characteristics of Tifinagh characters are given. In section 3 pre-processing process is described. Features extraction step is described in

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