DIGIT Recognition using Deep learning with the MNIST dataset in MATLAB

Introduction—

MATLABSolutions demonstrate In this task we are going to design The Implementing deep learning for recognition of hand written digit damage using MNIST dataset is one of the best to develop automatic system. Handwritten digit recognition system is implemented using computer so that it can recognize digits from the images, touch screens, papers and do classification in 10 predefined classes (0-9).Implementation of deep learning in this area has great importance. This digit recognition system can be used in in many areas such as postal mail sorting, bank check processing, hand recognition system is prepared to face challenge regarding writing style. We have used advanced method of CNN implementation to train model for the recognition because it performs better than tradition method.

Dataset —

Handwritten character recognition is an expansive research area that already contains detailed ways of implementation which include major learning datasets, popular algorithms, features scaling & feature extraction methods. MNIST dataset (Modified National Institute of Standards and Technology database) is the subset of the NIST dataset which is a combination of two of NIST's databases: Special Database 1 and Special Database 3. Special Database 1 and Special Database 3 consist of digits written by high school students and employees of the United States Census Bureau, respectively. MNIST contains a total of 70,000 handwritten.