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

Chao Chung Peng, Chao Yang Huang, Yi Ho Chen

研究成果: Article同行評審

摘要

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.

原文English
文章編號3809
期刊Electronics (Switzerland)
12
發行號18
DOIs
出版狀態Published - 2023 9月

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 訊號處理
  • 硬體和架構
  • 電腦網路與通信
  • 電氣與電子工程

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