Feature Reduction Using Binary PSO towards Recognition of Farsi Handwritten Digits

Recognition of handwritten digits is one of the most important problems in Optical Character Recognition (OCR) domain. In this paper a combination of two features، gradient histogram and modified characteristic Loci، are used for Persian handwritten digits. Furthermore، the most important features are selected using improved New Binary Particle Swarm Optimization algorithm (INBPSO) with an appropriate fitness function. SVM is used for classification of the digits. Having the selection pattern found in the training phase، the extracted features of the test samples are reduced using this pattern and then the final vector of the selected features are classified with the trained SVM model. The proposed method is applied on HODA database. Without reducing the features we achieved 99. 40% accuracy and after reducing، the accuracy of 99. 28% is reached. Comparing the results with the previous works، indicates that the proposed method has better performance in feature extraction and feature selection.