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.

在印度，将近1200万视障人士难以识别纸币。需要开发一种可以识别纸币并提供语音消息的应用程序。在本文中，开发了一种新颖的轻量级卷积神经网络（CNN）模型，用于有效的Web和移动应用程序来识别印度纸币。已创建了印度纸币的新数据集，以训练，验证和测试CNN模型。这种基于CNN的网络和移动应用程序将基于识别的纸币提供文本和音频输出。拟议的模型是使用TensorFlow开发的，并通过选择最佳超参数值进行了改进，并与使用转移学习的现有众所周知的CNN架构进行了比较。