使用深度学习的印度货币面额自动检测

Yash Patel, Ramakant Chhangani, Sarang Deshpande, Ramchand Hablani, Sweta Jain
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引用次数: 0

摘要

在没有UPI的情况下,识别要实际支付的货币纸币的面额是消费者向卖家付款的第一步。在这个项目中,我们提出了一种使用卷积神经网络检测印度货币面额的方法。计算机视觉与目标检测是当今世界研究的热点领域。它有许多应用,如机器缺陷检测、入侵者检测、代码计算机视觉和字符识别等。通过我们所做的工作,我们探索了一些对人们日常生活有很大帮助的东西。在这个项目中,我们试图研究使用卷积神经网络检测货币面额的方法。目标是建立一个能够有效检测印度货币面额的模型。一般来说,这个模型对视力受损的人很有用。实验结果表明,使用卷积神经网络是一种很好的方法,如果对模型进行训练,使其能够识别感兴趣的区域,则可以进一步改进模型。
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Automatic Detection of Indian Currency Denominations using Deep Learning
Identification of the denomination of the currency note to pay physically without UPI is the first step of paying to the seller by the consumer. In this project, we have proposed an approach to detect denominations of Indian currency using Convolutional Neural Networks. Computer Vision and object detection is an area of great interest for research in today’s world. It has several applications like detection of defects in machinery, intruder detection, computer vision for code and character recognition among many others. Through the work we have done, we explored something that could be of great help to people in day-to-day life. In this project we have tried to investigate the approaches to detect currency denominations using Convolutional Neural Networks. The objective is to build a model that would be able to detect Indian currency denominations efficiently. Typically the model will be useful for people with vision impairment. The experimental results show that the use of Convolutional Neural Networks is a good way and the model can further be improved if it is trained in such a way that it could also identify the regions of interest.
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来源期刊
International Journal of Next-Generation Computing
International Journal of Next-Generation Computing COMPUTER SCIENCE, THEORY & METHODS-
自引率
66.70%
发文量
60
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