Handwritten Amharic Character Recognition Through Transfer Learning: Integrating CNN Models and Machine Learning Classifiers
Handwritten Amharic character recognition presents significant challenges due to the script’s syllabic nature and variations in handwriting styles. This study investigates a hybrid approach that integrates convolutional neural networks (CNNs) with machine learning classifiers to enhance r...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10935359/ |
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| author | Natenaile Asmamaw Shiferaw Zefree Lazarus Mayaluri Prabodh Kumar Sahoo Ganapati Panda Prince Jain Adyasha Rath Md. Shabiul Islam Mohammad Tariqul Islam |
| author_facet | Natenaile Asmamaw Shiferaw Zefree Lazarus Mayaluri Prabodh Kumar Sahoo Ganapati Panda Prince Jain Adyasha Rath Md. Shabiul Islam Mohammad Tariqul Islam |
| author_sort | Natenaile Asmamaw Shiferaw |
| collection | DOAJ |
| description | Handwritten Amharic character recognition presents significant challenges due to the script’s syllabic nature and variations in handwriting styles. This study investigates a hybrid approach that integrates convolutional neural networks (CNNs) with machine learning classifiers to enhance recognition accuracy. Transfer learning is applied using four CNN architectures: AlexNet, VGG16, VGG19, and ResNet50 as feature extractors. Initially, their performance is evaluated with softmax classifiers. Subsequently, the softmax layer is replaced with machine learning classifiers, including Random Forest, XGBoost, and Support Vector Machine (SVM), while freezing the pretrained feature extractors. The Hybrid ResNet50 + SVM model achieves the highest accuracy of 91.89%, with a precision of 92.46%, recall of 91.15%, and an F1-score of 91.80%. These results indicate that SVM serves as a potential alternative to softmax, offering robust classification performance for complex handwritten scripts. This research contributes to advancements in handwritten character recognition systems for underrepresented languages. |
| format | Article |
| id | doaj-art-64a9dfb7212f4e8f8c3022aceefd3e09 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-64a9dfb7212f4e8f8c3022aceefd3e092025-08-20T02:54:22ZengIEEEIEEE Access2169-35362025-01-0113521345214810.1109/ACCESS.2025.355319910935359Handwritten Amharic Character Recognition Through Transfer Learning: Integrating CNN Models and Machine Learning ClassifiersNatenaile Asmamaw Shiferaw0https://orcid.org/0009-0002-3243-3401Zefree Lazarus Mayaluri1https://orcid.org/0000-0002-6910-126XPrabodh Kumar Sahoo2https://orcid.org/0000-0003-0112-7129Ganapati Panda3https://orcid.org/0000-0002-3555-5685Prince Jain4https://orcid.org/0000-0002-7950-7263Adyasha Rath5Md. Shabiul Islam6https://orcid.org/0000-0002-4630-7249Mohammad Tariqul Islam7https://orcid.org/0000-0002-4929-3209Department of Computer Science and Engineering, C. V. Raman Global University, Bhubaneswar, Odisha, IndiaDepartment of Electrical Engineering, C. V. Raman Global University, Bhubaneswar, Odisha, IndiaDepartment of Mechatronics Engineering, Parul Institute of Technology, Parul University, Vadodara, Gujarat, IndiaDepartment of Electronics and Communication Engineering, C. V. Raman Global University, Bhubaneswar, Odisha, IndiaDepartment of Mechatronics Engineering, Parul Institute of Technology, Parul University, Vadodara, Gujarat, IndiaDepartment of Computer Science and Engineering, C. V. Raman Global University, Bhubaneswar, Odisha, IndiaFaculty of Engineering, Multimedia University, Cyberjaya, Selangor, MalaysiaDepartment of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, UKM Bangi, Selangor, MalaysiaHandwritten Amharic character recognition presents significant challenges due to the script’s syllabic nature and variations in handwriting styles. This study investigates a hybrid approach that integrates convolutional neural networks (CNNs) with machine learning classifiers to enhance recognition accuracy. Transfer learning is applied using four CNN architectures: AlexNet, VGG16, VGG19, and ResNet50 as feature extractors. Initially, their performance is evaluated with softmax classifiers. Subsequently, the softmax layer is replaced with machine learning classifiers, including Random Forest, XGBoost, and Support Vector Machine (SVM), while freezing the pretrained feature extractors. The Hybrid ResNet50 + SVM model achieves the highest accuracy of 91.89%, with a precision of 92.46%, recall of 91.15%, and an F1-score of 91.80%. These results indicate that SVM serves as a potential alternative to softmax, offering robust classification performance for complex handwritten scripts. This research contributes to advancements in handwritten character recognition systems for underrepresented languages.https://ieeexplore.ieee.org/document/10935359/Amharic scripthandwritten character recognitiondeep learningconvolutional neural networks (CNN)transfer learning |
| spellingShingle | Natenaile Asmamaw Shiferaw Zefree Lazarus Mayaluri Prabodh Kumar Sahoo Ganapati Panda Prince Jain Adyasha Rath Md. Shabiul Islam Mohammad Tariqul Islam Handwritten Amharic Character Recognition Through Transfer Learning: Integrating CNN Models and Machine Learning Classifiers IEEE Access Amharic script handwritten character recognition deep learning convolutional neural networks (CNN) transfer learning |
| title | Handwritten Amharic Character Recognition Through Transfer Learning: Integrating CNN Models and Machine Learning Classifiers |
| title_full | Handwritten Amharic Character Recognition Through Transfer Learning: Integrating CNN Models and Machine Learning Classifiers |
| title_fullStr | Handwritten Amharic Character Recognition Through Transfer Learning: Integrating CNN Models and Machine Learning Classifiers |
| title_full_unstemmed | Handwritten Amharic Character Recognition Through Transfer Learning: Integrating CNN Models and Machine Learning Classifiers |
| title_short | Handwritten Amharic Character Recognition Through Transfer Learning: Integrating CNN Models and Machine Learning Classifiers |
| title_sort | handwritten amharic character recognition through transfer learning integrating cnn models and machine learning classifiers |
| topic | Amharic script handwritten character recognition deep learning convolutional neural networks (CNN) transfer learning |
| url | https://ieeexplore.ieee.org/document/10935359/ |
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