Principal Component Analysis-Based Logistic Regression for Rotated Handwritten Digit Recognition in Consumer Devices

Abstract

Handwritten digit recognition has been used in many consumer electronic devices for a long time. However, we found that the recognition system used in current consumer electronics is sensitive to image or character rotations. To address this problem, this study builds a low-cost and light computation consumption handwritten digit recognition system. A Principal Component Analysis (PCA)-based logistic regression classifier is presented, which is able to provide a certain degree of robustness in the digit subject to rotations. To validate the effectiveness of the developed image recognition algorithm, the popular MNIST dataset is used to conduct performance evaluations. Compared to other popular classifiers installed in MATLAB, the proposed method is able to achieve better prediction results with a smaller model size, which is 18.5% better than the traditional logistic regression. Finally, real-time experiments are conducted to verify the efficiency of the presented method, showing that the proposed system is successfully able to classify the rotated handwritten digit.