<p>In this paper, a graph based handwritten Tifinagh character recognition system is presented. In preprocessing Zhang Suen algorithm is enhanced. In features extraction, a novel key point extraction algorithm is presented. Images are then represented by adjacency matrices defining graphs where nodes represent feature points extracted by a novel algorithm. These graphs are classified using a graph matching method. Experimental results are obtained using two databases to test the effectiveness. The system shows good results in terms of recognition rate.</p>
<p>In this paper, the convolutional neural network (CNN) is used in order to design an efficient optical character recognition (OCR) system for the Tifinagh characters. indeed, this approach has proved a greater efficiency by giving an accuracy of 99%, this approach based in keys points detection using Harris corner method, the detected points are automatically added to the original image to create a new database compared to the basic method that use directly the database after a preprocessing step consisting on normalization and thinning the characters. Using this method, we can benefit from the power of the convolutional neural network as classifier in image that has already the feature. The test was performed on the Moroccan Royal Institute of Amazigh Culture (IRCAM) database composed of 33000 characters of different size and style what present the difficulty, the keys points are the same in the printed and handwritten characters so this method can be apply in both type with some modifications.</p>
In this paper a system for the recognition of printed Tifinagh characters is presented. It is divided into three main steps: preprocessing, feature extraction, and classification. Image quality is enhanced through preprocessing which are: binarization, normalization and thinning. Then the image is given to a proposed structural feature extracting algorithm where the character is divided into several geometrically sample shapes which are segments, then transformed into an undirected graph with unique coordinate of all nodes. The character is classified by matching the graph of the character and its counterpart graph which is generated from the images in the IRCAM database using an efficient spectral graph matching algorithm. Experimental results and analysis are accomplished by the use of 3267 random characters to test the effectiveness. The system shows good results in term of accuracy and CPU time.
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