A Recognition System for Handwritten Digits Using CNN

This paper presents a model of integrating the synergy of two superior classifiers: Convolutional Neural Network (CNN) and Random Forest Classifier (RFC), which have proven results in recognizing different types of patterns. Handwritten digit recognition is one of the practically important issues in pattern recognition applications. The applications of digit recognition include in postal mail sorting, bank check processing, form data entry, etc. The heart of the problem lies within the ability to develop an efficient algorithm that can recognize hand written digits and which is submitted by users by the way of a scanner, tablet, and other digital devices. The problem of handwritten digit recognition has long been an open problem in the field of pattern classification. Several studies have shown that Neural Network has a great performance in data classification. Ability for accurate digit recognizer modelling and prediction is critical for pattern recognition and security. A variety of classification machine learning algorithms are known to be effective for digit recognition.