Finally, predicting the classes in an input image by processing the image into a desired format and then feeding it into the trained neural network.Ĭonventional neural networks have been demonstrated to be a powerful framework for background subtraction in video acquired by static cameras. The prime focus of this project is firstly to design a convolutional neural network with suitable parameters and train it with a dataset of our own. Dataset Preparation, Image processing of a certain level and a Convolutional Neural Network as a classifier are the three main areas, in which our project is completely relying on. So, basically this project is a way to digitize the information in an image for the purpose of convenient retrieval and efficient processing of data. Our project mainly focuses on recognizing the digits and characters in an image. The report discusses various fundamentals and an implementation technique to build the offline system that recognize the paper notes of Nepali Currency. This report represents the details on a project entitled “CASH RECOGNITON SYSTEM USING CONVOLUTION NEURAL NETWORK WITH TRANSFER LEARNING” as a part of the curriculum for the final year project of B.E. We conclude with an explanation of the obtained results, relating the structures of the different networks with their performance and training cost. All three networks reach over 98% classification accuracy, going as far as 99.52% in the case of the best one. Finally, we create three convolutional networks to tackle MNIST, detailing how such a task is approached with TensorFlow and the workflow followed. We then pave the way for the problem of image classification: we comment several higher-level TensorFlow wrappers (focusing on Slim, a library born within Google itself which is used in the last part of the project), describe the basic principles of convolutional networks and introduce the MNIST problem (automatic handwritten digit recognition), outlining its history and current state of the art. Through the first example of a deep network, we illustrate the theoretical and TensorFlow-related elements described earlier, applying them to the problem of classifying flowers of the Iris species. After that, we delve into the computational approach, explaining TensorFlow’s operation principles and the necessary concepts for its use, namely the computational graph, variables and execution sessions. Special attention is paid to various regularisation methods which are applied later on. We begin by laying the theoretical foundations of these networks, covering their motivation, techniques used and some mathematical aspects of their training. This project explores deep artificial neural networks and their use with Google’s open-source library TensorFlow.
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