Digit Classification Using HOG Features

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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 pre-processing, 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


Digit recognition is a subset of Optical Character Recognition (OCR). Digit recognition can be divided into two types, handwritten and fixed font printed digit recognition. However, the main challenge of handwritten digit recognition is to deal with wide variety of writing styles. The objective of handwritten digit recognition is to read digits from scanned images such as envelope, license plate, bank cheque etc. and digitalize them for storing and further using . Handwritten digit recognition is more difficult comparing to printed digits as the size, shape and style of digits vary from man to man. The accuracy of classification and recognition largely depends on the feature extraction method. In this paper Histogram of Oriented Gradient feature extraction technique is used to obtain features.



In this paper, comparative performance of three well-known kernels of the SVM classification algorithm has been investigated to find out the appropriate kernel function for the used sample dataset of Bangla handwritten digits. Experimental result shows that using HOG features, handwritten digit recognition shows at most 97.08% accuracy for polynomial kernel function. This performance mostly depends on the preprocessing and feature extraction techniques. However, the recognition rate can be improved using the combination of more than one feature extraction technique.

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