构建斯里兰卡硬币图像数据集，验证深度学习在货币识别中的高精度表现。
SerendibCoins: Exploring The Sri Lankan Coins Dataset
- 构建首个全面的斯里兰卡硬币图像数据集，支持多模型对比实验。
- 卷积神经网络达到近乎完美的分类准确率，错误极少。
- 适合研究区域性货币识别与深度学习应用的开发者和学者。
硬币识别与分类在金融及自动化系统中至关重要。本研究提出一个全面的斯里兰卡硬币图像数据集，并评估其对机器学习模型分类性能的影响。我们测试了K近邻（KNN）、支持向量机（SVM）、随机森林等传统机器学习分类器，以及自研卷积神经网络（CNN）在不同分类层级上的表现。结果表明，SVM在传统方法中优于KNN和随机森林；而CNN模型实现近乎完美的分类准确率，误判极少。该数据集显著提升了自动化硬币识别系统的性能，为区域货币分类与深度学习应用提供了坚实基础。
原文摘要 · Abstract (English)
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.
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