A Haar Wavelet-Based Zoning For Offline Arabic Handwritten Character Recognition

Abstract

Due to the nature of handwriting with high degree of variability and imprecision, obtaining features that represent characters is a difficult task. In this research, a features extraction method for handwritten Arabic Character recognition is investigated. Its major goal is to maximize the recognition rate with the least amount of elements. This method compute the 1 level Haar Wavelet Transform for Binary character image, then divide the Wavelet space into 8 Zones, for each Zone, three features have been extracted: mean, standard division, and skewness. The Recognition have been done using Mahalanobis distance. The proposed method provides good recognition accuracy of 73% for handwritten characters even with fewer train samples