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Automatic Indian currency denomination recognition system based on artificial neural network

机译:基于人工神经网络的自动印度货币面额识别系统

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Automatic detection and recognition of Indian currency note has gained a lot of research attention in recent years particularly due to its vast potential applications. In this paper we introduce a new recognition method for Indian currency using computer vision. It is shown that Indian currencies can be classified based on a set of unique non discriminating features such as color, dimension and most importantly the Identification Mark (unique for each denomination) mentioned in RBI guidelines. Firstly the dominant color and the aspect ratio of the note are extracted. After this the segmentation of the portion of the note containing the unique I.D. Mark is done. From these segmented image, feature extraction is done using Fourier Descriptors. As each note has a unique shape as the I.D. Mark, the classification of these shapes is done with the help of Artificial Neural Network. After feature extraction, the denominations are recognized based on the developed algorithm. The success rate of the proposed system is 97% requiring a processing time of 2.52 seconds.
机译:近年来,印度货币票据的自动检测和识别尤其是由于其巨大的潜在应用。在本文中,我们使用计算机愿景介绍了印度货币的新识别方法。结果表明,印度货币可以基于一组独特的非辨别特征,例如颜色,维度,最重要的是,RBI指南中提到的识别标记(每个面额的唯一)。首先,提取备注的主导颜色和宽高比。在此之后,包含独特的I.D的音符部分的分割。标记完成了。从这些分段图像,使用傅立叶描述符完成特征提取。由于每个音符具有独特的形状作为I.D.标记,这些形状的分类是在人工神经网络的帮助下完成的。特征提取后,基于发达的算法识别面额。所提出的系统的成功率为97%,需要加工时间为2.52秒。

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