Arabic Handwriting Texture Analysis for Writer Identification Using the DWT-Lifting Scheme

Abstract

In this paper, we present an approach for writer identification using off-line Arabic handwriting. The proposed method explores the handwriting texture analysis by 2D discrete wavelet transforms using lifting scheme. A comparative evaluation between textural features extracted by 9 different wavelet transform functions was done. A modular multilayer perceptron classifier was used. Experiments have shown that writer identification accuracies reach best performance levels with an average rate of 95.68%. Experiments have been carried out using a database of 180 text samples. The chosen text was made to guarantee the involvement of the various internal shapes and letter locations within an Arabic subword.