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Trained convolutional neural network based on selected beta filters for Arabic letter recognition

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Abstract This paper presents a fast deep learning approach to segment and recognize off‐line Arabic printed and handwritten letters from words. We proposed a simple and powerful algorithm for Arabic letter segmentation based on vertical profile and baseline analysis. Then, we proposed a new method for feature extraction using fast wavelet transform. These extracted features are exploited as connection weights to build a convolutional neural network for each letter shape. Finally, all estimated model shapes are boosted to increase the robustness and performance of the proposed system. The proposed approach was tested on APTI and IESK‐arDB databases to evaluate performance for printed letters and handwritten letters, respectively. The obtained results show the robustness of our approach as well as the speed of the proposed recognition algorithm for both databases. This article is categorized under: Technologies > Machine Learning Technologies > Computational Intelligence Technologies > Classification
The Arabic alphabet and their corresponding shapes
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Fonts: (a) Advertising Extra Bold, (b) Diwani, (c) Unicode Sara M, and (d) Times
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The different Arabic characters classes
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Effect of the abstraction level analysis number on the performance of recognition system
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Example of an Arabic letter with different styles
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Some examples of Arabic handwritten words
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Feature extraction based on convolutional dyadic MRA
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Example of Arabic letter segmentation from a semi‐cursive word
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Example of Arabic letter segmentation from a cursive word
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Overview of the proposed approach
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Technologies > Classification
Technologies > Computational Intelligence
Technologies > Machine Learning

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