Arabic Character Recognition Based M-SVM: Review

Abstract

Abstract. Optical Character Recognition Systems (OCR) provide humanmachine interaction and are widely used in many applications. Classification is the most important step in an OCR system. Support Vectors Machines (SVM) is among the tool of classification that appears these days. This tool proves its ability to discriminate between the forms and gives encouraging result. In this paper, we present an overview of the Arabic optical character recognition (AOCR) work done using SVM classifiers.

Recognition of Tifinagh Characters Using Optimized Convolutional Neural Network

“…The Fig. 6 shows that the non-respect of the standards of writing of the grapheme of a character causes an ambiguity to classify the characters either for the machine (the algorithm) or for the human (taking example 2 of the same figure the real class is yakw , but the system predicted yagw (same problem for 1, 3,5,6). For sample 4 in Fig.…”

“…The last phase, called classification, recognizes the content of the input image based on the features computed in extraction phase and the machine learning algorithms. Neural Network (NN) and Support Vector Machine (SVM) are two examples of algorithms which can be used in the classification phases [4,5]. Neural Network is an example of Machine Learning algorithms, it is formed from a set of interconnected neurons, this group of Table 1 Tifinagh characters adopted by IRCAM neurons is structured on layers: input layer, hidden layers and output layer.…”

Printed Arabic Script Recognition: A Survey

“…SVM, which is a binary classifier, has been used in the implementation of printed Arabic OCR systems [106], [95], [115]. (For a comprehensive review of applying SVM to Arabic OCR, refer to [116]). However, classifiers based on SVM are mostly applied to a small set of data due to the high complexity of training and processing time [117], [118].…”

Printed Arabic Text Recognition using Linear and Nonlinear Regression

“…Experimental results have shown that the proposed classification engine outperforms RBF based classifiers and ART1-based classifiers. www.ijacsa.thesai.org Amara et al [11] have overviewed Arabic OCR using Support Vectors Machines (SVM). Although, SVM has proven its efficiency in different domains among other classification tools, SVM has not been effectively applied in recognizing Arabic characters.…”