Principal Component Analysis-Based Logistic Regression for Rotated Handwritten Digit Recognition in Consumer Devices
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 computatio...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/18/3809 |
| _version_ | 1797580437357330432 |
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| author | Chao-Chung Peng Chao-Yang Huang Yi-Ho Chen |
| author_facet | Chao-Chung Peng Chao-Yang Huang Yi-Ho Chen |
| author_sort | Chao-Chung Peng |
| collection | DOAJ |
| description | 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 <span style="font-variant: small-caps;">MATLAB</span>, 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. |
| first_indexed | 2024-03-10T22:50:56Z |
| format | Article |
| id | doaj.art-25dce182f9b64300b75acfa15dcc830c |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T22:50:56Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-25dce182f9b64300b75acfa15dcc830c2023-11-19T10:21:38ZengMDPI AGElectronics2079-92922023-09-011218380910.3390/electronics12183809Principal Component Analysis-Based Logistic Regression for Rotated Handwritten Digit Recognition in Consumer DevicesChao-Chung Peng0Chao-Yang Huang1Yi-Ho Chen2Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan 70101, TaiwanDepartment of Aeronautics and Astronautics, National Cheng Kung University, Tainan 70101, TaiwanDepartment of Aeronautics and Astronautics, National Cheng Kung University, Tainan 70101, TaiwanHandwritten 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 <span style="font-variant: small-caps;">MATLAB</span>, 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.https://www.mdpi.com/2079-9292/12/18/3809Principal Component Analysislogistic regressionrotated imagehandwritten digit recognition |
| spellingShingle | Chao-Chung Peng Chao-Yang Huang Yi-Ho Chen Principal Component Analysis-Based Logistic Regression for Rotated Handwritten Digit Recognition in Consumer Devices Electronics Principal Component Analysis logistic regression rotated image handwritten digit recognition |
| title | Principal Component Analysis-Based Logistic Regression for Rotated Handwritten Digit Recognition in Consumer Devices |
| title_full | Principal Component Analysis-Based Logistic Regression for Rotated Handwritten Digit Recognition in Consumer Devices |
| title_fullStr | Principal Component Analysis-Based Logistic Regression for Rotated Handwritten Digit Recognition in Consumer Devices |
| title_full_unstemmed | Principal Component Analysis-Based Logistic Regression for Rotated Handwritten Digit Recognition in Consumer Devices |
| title_short | Principal Component Analysis-Based Logistic Regression for Rotated Handwritten Digit Recognition in Consumer Devices |
| title_sort | principal component analysis based logistic regression for rotated handwritten digit recognition in consumer devices |
| topic | Principal Component Analysis logistic regression rotated image handwritten digit recognition |
| url | https://www.mdpi.com/2079-9292/12/18/3809 |
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