A Novel Federated Learning-Based Image Classification Model for Improving Chinese Character Recognition Performance
Chinese characters are an essential means of communication in the East Asian cultural regions. Chinese characters are characterized by many strokes and complex structures, some of which are very similar. However, the misrecognition of messy writing can significantly decrease the accuracy of Optical...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10787009/ |
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| Summary: | Chinese characters are an essential means of communication in the East Asian cultural regions. Chinese characters are characterized by many strokes and complex structures, some of which are very similar. However, the misrecognition of messy writing can significantly decrease the accuracy of Optical Character Recognition (OCR) systems. Therefore, filtering the messy writing is critical. We propose a novel federated learning-based deep learning model to classify messy Chinese handwriting, addressing the challenges posed by complex character structures and variations in writing quality. To validate the proposed method, we conducted experiments to compare our approach with global, local, FedAVG, and IPA federated learning methods. We evaluated our approach using the CNN, ResNet50V2, NASNetLarge, EfficientNetV2B0, EfficientNetB0, and swin transformer models. The results showed a 301.2% improvement in the test accuracy and a 241.54% increase in the area under the curve (AUC) score with the proposed method, particularly when EfficientNetB0 was trained using the global method. These findings confirm that the proposed model effectively classifies Chinese messy handwriting. Additionally, when the data were fed into the OCR system, the match rate between the image and the recognized characters improved by up to 7.14%. |
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| ISSN: | 2169-3536 |