Digit Recognition is an essential element of the process of scanning and converting\ndocuments into electronic format. In this work, a new Multiple-Cell\nSize (MCS) approach is being proposed for utilizing Histogram of Oriented\nGradient (HOG) features and a Support Vector Machine (SVM) based classifier\nfor efficient classification of Handwritten Digits. The HOG based technique\nis sensitive to the cell size selection used in the relevant feature extraction\ncomputations. Hence a new MCS approach has been used to perform\nHOG analysis and compute the HOG features. The system has been tested on\nthe Benchmark MNIST Digit Database of handwritten digits and a classification\naccuracy of 99.36% has been achieved using an Independent Test set\nstrategy. A Cross-Validation analysis of the classification system has also been\nperformed using the 10-Fold Cross-Validation strategy and a 10-Fold classification\naccuracy of 99.26% has been obtained. The classification performance\nof the proposed system is superior to existing techniques using complex procedures\nsince it has achieved at par or better results using simple operations in\nboth the Feature Space and in the Classifier Space. The plots of the system�s\nConfusion Matrix and the Receiver Operating Characteristics (ROC) show\nevidence of the superior performance of the proposed new MCS HOG and\nSVM based digit classification system.
Loading....