Persian Handwritten Digit Recognition Using Ensemble Classifiers

Optical character recognition (OCR) includes three main sections, pre-processing, feature extraction and classification. The purpose of the pre-processing is to remove noise, smooth and normalize the input data, which can have a significant role in better differentiating patterns in the feature space. In the feature extraction, a feature vector is assigned to each sample which represents the sample in the related feature space and thus makes it distinct from the other samples. Feature extraction has significant effect on classification of sample class. In the classification stage, correct boundaries should be made between feature vectors, so that the samples of each pattern are separated from other samples by clear boundaries. Persian handwritten digits recognition is a branches of pattern recognition. In this paper, a method is proposed to recognize Persian handwritten digits. The proposed framework includes three main sections, pre-processing, feature extraction and classification. In the feature extraction stage, an appropriate and complementary set of features consist of 115 features extracted from Persian handwritten digits. In the classification stage, the ensemble classifier algorithm is used to separate the samples’ classes from each other. Estimation of results was performed on TMU (Tarbiat Modares University) digits database and the best recognition rate of Persian handwritten digits, was 95.280%.