A Comprehensive Review on Handwritten Digit Recognition using various Neural Network Approaches
Handwritten digit recognition has capability of the computer to receive information from sources such as paper documentation, camera, tablets and other devices. Handwritten digits are used in different types of data like cheque, post mail, address on home's name plate and exam form which also filled by hand etc. In recent years, handwritten digit recognition plays a main role for user authentication applications. In the proposed work, we will be developed gradient descent back propagation using neural network based on digit recognition system. Here introducing four phase: preprocessing, segmentation, feature extraction, classification. MNIST dataset contains CSV file from which image can be obtained by reading it. And this image acts as an input to the procedure. After that this image is preprocessed using RGB converter into binary form and contains some noise ,this noise will be remove using median filter and then convert into filtered image and also various techniques such as skeletonization, centering, and thresholding, are used for the preprocessing the image numerals If the characteristics for one class are distinct and remain constant with variations in this class, then we can term the features as good and desired. Neural Network Technique is used for classification and recognition of handwritten digits through training and testing.