Neural Networks based Handwritten Digit Recognition

In the field of Artificial Intelligence, scientists have made many enhancements that helped a lot in the development of millions of smart devices. On the other hand, scientists brought a revolutionary change in the field of image processing and one of the biggest challenges in it is to identify data in both printed as well as hand-written formats. One of the most widely used techniques for the validity of these types of document is NEURAL NETWORKS. Neural networks currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. Handwritten Digit Recognition is an extensively employed method to transform the data of handwritten form into digital format. This data can be used anywhere, in any field, like database, data analysis, etc. There are multitude of techniques introduced now that can be used to recognize handwriting of any form. In the suggested system, we will handle the issue of machine reading numerical digits using the technique of NEURAL NETWORKS. We aim to learn the basic functioning of the neural network and expect to find the correlation between all the parameters i.e. No of layers, Layer Size, Learning Rate, Size of the Training sets and the associated accuracy achieved by the neural net in identifying the handwritten digits, by comparing the accuracy achieved on different sets of the four variable parameters varying in the suitable range.