This paper is published in Volume-5, Issue-1, 2019
Area
Computer Science And Engineering
Author
Meera Madhu
Co-authors
Dr. N. Yuvaraj, Indhumathi P., Gokula Priya S.
Org/Univ
KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
Pub. Date
06 February, 2019
Paper ID
V5I1-1274
Publisher
Keywords
Accuracy, Back-propagation, Feed-forward, Multilayer perceptron, Neural Network, Deep learning, MNIST Database, Digit recognition

Citationsacebook

IEEE
Meera Madhu, Dr. N. Yuvaraj, Indhumathi P., Gokula Priya S.. Enhancing the accuracy of digit recognition using machine learning algorithms, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Meera Madhu, Dr. N. Yuvaraj, Indhumathi P., Gokula Priya S. (2019). Enhancing the accuracy of digit recognition using machine learning algorithms. International Journal of Advance Research, Ideas and Innovations in Technology, 5(1) www.IJARIIT.com.

MLA
Meera Madhu, Dr. N. Yuvaraj, Indhumathi P., Gokula Priya S.. "Enhancing the accuracy of digit recognition using machine learning algorithms." International Journal of Advance Research, Ideas and Innovations in Technology 5.1 (2019). www.IJARIIT.com.

Abstract

Handwritten character recognition is one of the important issues in pattern recognition applications. The applications of digit recognition include postal mail sorting, bank check processing, data entry form, pin code identification, identify the doctors' prescriptions etc. The conversion of an image based on the digit into letter codes for further use in a computer or text processing application is the first step in an off-line handwriting recognition system. This paper presents an approach of (MLP) Multilayer Perceptron neural network to recognize and predict handwritten digits from 0 to 9. MLP is a class of feed-forward artificial neural network. It consists of at least three layers of nodes having an input layer, a hidden layer, and an output layer. Each node is a neuron that uses a nonlinear activation function. The MNIST database is a large database of handwritten digits that is commonly used for training various image processing systems contain 60,000 training images and 10,000 testing images. The dataset was trained using gradient descent back-propagation algorithm and further tested using the feed-forward algorithm. The system performance is observed by varying the number of hidden layers. Various algorithms used for image processing have been discussed. The main objective of this paper is to ensure effective and reliable approaches for recognition of handwritten digits.