SerendibCoins: Exploring The Sri Lankan Coins Dataset
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908378683408384 |
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| author | Wanigasingha, NH Sithpahan, ES Ariyaratne, MKA De Silva, PRS |
| author_facet | Wanigasingha, NH Sithpahan, ES Ariyaratne, MKA De Silva, PRS |
| contents | The recognition and classification of coins are essential in numerous financial and automated systems. This study introduces a comprehensive Sri Lankan coin image dataset and evaluates its impact on machine learning model accuracy for coin classification. We experiment with traditional machine learning classifiers K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forest as well as a custom Convolutional Neural Network (CNN) to benchmark performance at different levels of classification. Our results show that SVM outperforms KNN and Random Forest in traditional classification approaches, while the CNN model achieves near-perfect classification accuracy with minimal misclassifications. The dataset demonstrates significant potential in enhancing automated coin recognition systems, offering a robust foundation for future research in regional currency classification and deep learning applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18634 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SerendibCoins: Exploring The Sri Lankan Coins Dataset Wanigasingha, NH Sithpahan, ES Ariyaratne, MKA De Silva, PRS Computer Vision and Pattern Recognition The recognition and classification of coins are essential in numerous financial and automated systems. This study introduces a comprehensive Sri Lankan coin image dataset and evaluates its impact on machine learning model accuracy for coin classification. We experiment with traditional machine learning classifiers K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forest as well as a custom Convolutional Neural Network (CNN) to benchmark performance at different levels of classification. Our results show that SVM outperforms KNN and Random Forest in traditional classification approaches, while the CNN model achieves near-perfect classification accuracy with minimal misclassifications. The dataset demonstrates significant potential in enhancing automated coin recognition systems, offering a robust foundation for future research in regional currency classification and deep learning applications. |
| title | SerendibCoins: Exploring The Sri Lankan Coins Dataset |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.18634 |