孟加拉塔卡:孟加拉国纸币分类的数据集
Md Naimul Islam Nuhash1, Sadia Akter1
1Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Savar, Dhaka 1216, Bangladesh.
Data in brief
|July 24, 2025
概括
本研究介绍了BanglaNotes,这是一个用于自动分类孟加拉国纸币的新数据集. 它提供高质量的图像,用于为金融安全应用程序训练机器学习模型.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 手动验证纸币的真实性是低效的,容易出现错误.
- 自动化系统对于金融安全和电子交易至关重要.
- 需要专门的数据集来训练可靠的货币分类模型.
研究的目的:
- 介绍BanglaNotes,这是孟加拉国纸币面额分类的基准数据集.
- 提供高质量的标记数据集,用于训练和测试机器学习算法.
- 促进金融自动化和货币识别的深度学习研究.
主要方法:
- 编制了一套由5073张孟加拉国纸币高质量图像组成的数据集.
- 包括九个面额 (2, 5, 10, 20, 50, 100, 200, 500, 1000 BDT) 在内.
- 确保一致的表现,并将每个图像标记为其代价.
主要成果:
- 孟加拉票据数据集现在可供公众使用.
- 该数据集支持开发用于货币分类的机器学习模型.
- 它作为银行和移动支付中的现实应用的基准.
结论:
- 孟加拉纸币公司推进了对纸币自动分类的研究.
- 该数据集将促进金融自动化和深度学习算法的创新.
- 公开发布鼓励进一步开发强大的货币识别系统.
相关概念视频
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