Dyadic Multi-resolution Analysis-Based Deep Learning for Arabic Handwritten Character Classification

The problem addressed in this paper is the classification and recognition of Arabic handwritten characters. As a solution, we present a Neural Network (NN) architecture based on Fast Wavelet Transform (FWT) and Adaboost algorithm. FWT is used to extract character´s features, based on Multi-Resolution Analysis (MRA) at different levels of abstraction. These features are used to calculate inputs of hidden layer. After this first step, the features are filtered, using Adaboost algorithm, to select the best corresponding ones to each shape of input characters. The reported results are tested on Arabic handwritten characters dataset with 6000 characters. The classification rate for the different groups of characters are 93.92%. Additionally, the speed of the classification algorithm is tested and reported.