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Coinnet: platform independent application to recognize Indian currency notes using deep learning techniques

机译:Coinnet:平台独立应用,以识别使用深度学习技术的印度货币票据

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In India, nearly 12 million visually impaired people had difficulty in identifying the currency notes. There is a need to develop an application that can recognize the currency note and provide a vocal message. In this paper, a novel lightweight Convolutional Neural Network (CNN) model is developed for efficient web and mobile applications to recognize the Indian currency notes. A new dataset for Indian currency notes has been created to train, validate, and test the CNN model. This CNN based web and mobile applications will provide a text and audio output based on the recognized currency note. The proposed model is developed using TensorFlow and improved by selection of optimal hyperparameter value, and compared with existing well known CNN architectures using transfer learning. Based on the results it has been observed that proposed model perform well over six widely used existing architectures in terms of training and testing accuracy.
机译:在印度,近1200万人视障人士难以识别货币票据。有必要开发一个可以识别货币票据并提供声音信息的应用程序。在本文中,开发了一种新颖的轻量级卷积神经网络(CNN)模型,用于高效的Web和移动应用来识别印度货币票据。已创建印度货币注释的新数据集以培训,验证和测试CNN模型。基于CNN的Web和移动应用程序将基于所公认的货币票据提供文本和音频输出。所提出的模型是使用Tensorflow开发的,通过选择最佳的超参数值改进,并与使用传输学习的现有众所周知的CNN架构进行比较。根据结果​​,已经观察到,在训练和测试准确性方面,所提出的模型在六种广泛使用的现有架构中表现出良好的。

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