Neural Networks for Handwritten character and Digits
Abstract: This article chronicles the development of an artificial neural network designed to recognize handwritten digits. Although some theory of neural networks is given here, it would be better if you already understood some neural network concepts, like neurons, layers, weights, and back propagation. The neural network described here is not a general-purpose neural network, and it's not some kind of a neural network workbench. Rather, we will focus on one very specific neural network (a five-layer convolution neural network) built for one very specific purpose (to recognize handwritten digits).
The idea of using neural networks for the purpose of recognizing handwritten digits is not a new one. The inspiration for the architecture described here comes from articles written by two separate authors. The first is Dr. Yann LeCun, who was an independent discoverer of the basic back propagation algorithm. Dr. LeCun hosts an excellent site on his research into neural networks. In particular, you should view his "Learning and Visual Perception" section, which uses animated GIFs to show results of his research. The MNIST database (which provides the database of handwritten digits) was developed by him. I used two of his publications as primary source materials for much of my work, and I highly recommend reading his other publications too (they're posted at his site). Unlike many other publications on neural networks, Dr. LeCun's publications are not inordinately theoretical and math-intensive; rather, they are extremely readable, and provide practical insights and explanations.