Handwritten Digit Recognition

The main objective of this paper is to recognize and predict handwritten digits from 0 to 9 where data set of 5000 examples of MNIST was given as input.

As we know as every person has different style of writing digits humans can recognize easily but for computers it is comparatively a difficult task so here we have used neural network approach where in the machine will learn on itself by gaining experiences and the accuracy will increase based upon the experience it gains.

The dataset was trained using feed forward neural network algorithm. The overall system accuracy obtained was 95.7%

by Jyoti Shinde | Chaitali Rajput | Prof. Mrunal Shidore | Prof. Milind Rane "Handwritten Digit Recognition"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018,

URL: http://www.ijtsrd.com/papers/ijtsrd8384.pdf

Direct Link - http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/8384/handwritten-digit-recognition/jyoti-shinde

Electronics & Communication Engineering, Engineering Journal