Advances in technology have replaced people in almost every field with machines. Thanks to the introduction of machines, banking automation has reduced the burden on humans. Banking automation requires more attention to declining currency handling. When the banknote is blurred or defaced, it is difficult to identify its currency value. A sophisticated design is included to increase the security of the call. This makes the call recognition task very difficult. For correct currency recognition, it is very important to choose a good function and an appropriate algorithm. One of the main problems that blind people face is the recognition of money, especially cash. In a way, the seemingly weakened people do not think about cash settlement and run into problems related to cash transactions in their daily life. It is a useful treatment for those who are externally weakened. studies and trialswere conducted according to key points, such as watermarks, images printed on money, the value of words and numbers, and the total amount of information gathering that stimulated CNN .This paper focuses on the study of solving social problems using Convolutional Neural Networks (CNNs) and validating and evaluating different CNN models. Here, the Alexnet, Googlenet and Vgg16 models were considered for the study .All models were adjusted during preparation and testing of individual data sets. Among these three models, Alexnet had the best performance, Vgg16 model showed 100% performance, and Google net showed performance with 88%