Own Handwritten Digit recognition using MLP and CNN in tensorflow
Deepti Nikumbh, Rupali Santosh Kale
Digit recognition,MLP,CNN
Object recognition in image is very popular and is widely used in almost all image processing applications. Handwritten digit recognition system is one such application. This paper presents an approach on developing a handwritten digit recognition system using multi-layer neural network and Convolutional Neural Network. These neural network models are trained and tested using the MNIST dataset. Further a real time dataset of authors own handwritten digit where used to test the performance of the system , a comparison of two deep learning models in terms of accuracy i.e successfully classifying digits between 0-9 and computational time taken is presented. The neural network models are developed in python using tensorflow a machine learning library.
Article Details
Unique Paper ID: 148591

Publication Volume & Issue: Volume 6, Issue 3

Page(s): 180 - 184
Article Preview & Download

Share This Article

Join our RMS

Conference Alert

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

Call For Paper

Volume 11 Issue 1

Last Date for paper submitting for Latest Issue is 25 June 2024

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews