Abstract
This paper presents results obtained by applying two neural networks models Backpropagation (BP), and Self-Organized Feature Map (SOFM) to a new application of handwritten Arabic alphanumeric character (HAAC) recognition. A novel method for features extraction, based on a shadow projection is used. Both networks are trained using Arabic character samples written by different people (learning set). They are required, after the learning is over, to recognize characters out of the learning set. Evaluation of the recognition (classification) capability of the two models for 28 alphanumeric characters is achieved. Depending on the experimental results, a comparison of both algorithms is done.
Highlights
Automatic recognition of handwriting has become an important discipline at the beginning of the 21st century
This paper is organized as follows: In section 2, we describe the use of Backpropagation (BP), and self-organized feature map (SOFM) networks
The Backpropagation (BP) and self-organized feature map (SOFM) learning algorithms are applied in handwritten Arabic alphanumeric character (HAAC) recognition
Summary
Automatic recognition of handwriting has become an important discipline at the beginning of the 21st century. From a practical point of view, accurate recognition should be made even if the handwritten style is deformed, or some characters closely reseble each other. Several literatures were discussed the subject of Arabic characters recognition in detail as in [2,3,4,5]. Kamel et al [7], have proposed a multiple classification architecture for handwritten Arabic characters recognition. Khalil [8], and Sharaidah [9], have reported on experiments in handwritten Arabic character recognition using neural networks. Amin [10], has presented a survey of Arabic characters recognition.
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