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