Bangla Handwritten Digit Classification and Recognition Using SVM Algorithm with HOG Features

This paper presents the basic approach of multiclass classification for handwritten digit recognition using Support Vector Machine and a comparative accuracy analysis for three well known kernel functions (linear, RBF and polynomial) and feature vectors corresponding to different cell sizes. However, the process of digit recognition includes several basic steps such as preprocessing, feature extraction and classification. Among them, feature extraction is the fundamental step for digit classification and recognition as accurate and distinguishable feature plays an important role to enhance the performance of a classifier. Histogram of Oriented Gradient (HOG) feature extraction technique has been used here. Therefore, for various cell sizes, the experimental results show around 98-100% accuracy for trained data and 91-97% accuracy for test set data according to various kernel functions. The target of this paper is to select a kernel function best suited for a particular resolution of image.