Issue Number: Vol. 4, No. 4
Year of Publication: 2014
Page Numbers: 486-492
Authors: Mohsen Biglari, Faezeh Mirzaei, Jalil Ghavidel Neycharan
Journal Name: International Journal of Digital Information and Wireless Communications (IJDIWC)
- Hong Kong
DOI:  http://dx.doi.org/10.17781/P001345


Automated handwritten character recognition seems to be necessary due to the increasing number of Persian/Arabic handwritten documents. A new approach for Persian/Arabic handwritten digit recognition has been proposed in this paper. This approach employs Local Binary Pattern (LBP) operator as base feature extraction method. Although this operator has shown great performance in research areas such as context and object recognition, but it has not been used in Persian/Arabic handwritten digit recognition problem. First step in the proposed approach involves smoothing, converting black and white input image to grayscale intensity image and resizing it to a fixed size. In the next step, input image is divided into several blocks. LBP operator is applied to each block to extract features. Finally, these features are used to train a multi-layer perceptron neural network with circular approach. Empirical results on Hoda dataset shows that the proposed approach has a very good generalization accuracy (99.72%) on Hoda dataset with 60000 train and 20000 test samples. This accuracy is the best among the state-of-the-art methods.