Handwritten Digit Recognition using Machine Learning

Abstract

A Handwritten Digit Recognition is one of the essentially crucial drawbacks in pattern recognition applications. The applications of digit recognition consists of form data entry, processing bank checks, postal mail sorting etc. The purpose of the obligation exists within the potential to develop an effective algorithm that can recognize the hand written digits and which is submitted to the users by the way of a tablet, scanner, and other electronic devices. Handwritten digit dataset is unclear in nature because there may not always be acute and faultlessly straight lines. One of the key objectives of handwritten digit recognition is feature extraction, which aims to eliminate redundant information from the input and provide a word picture that is more effectively represented by a collection of numerical properties. It deals with the majority of the critical information extraction from the raw picture data. The best algorithm is developed via two phases: "training," in which training data are used to create an algorithm capable of discriminating between groups previously defined by the operator (e.g., patients vs. controls), and "testing," in which the algorithm is used to blindly predict the group to which a new perception belongs. Additionally, it develops ample search area for the proper classification of future data characteristics and offers a very accurate classification performance over the training records.