Own Handwritten Digit recognition using MLP and CNN in tensorflow
Author(s):
Deepti Nikumbh, Rupali Santosh Kale
Keywords:
Digit recognition,MLP,CNN
Abstract
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

Conference Alert

NCSST-2021

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2021

SWEC- Management

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2021

Go To Issue



Call For Paper

Volume 10 Issue 1

Last Date for paper submitting for March Issue is 25 June 2023

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

Contact Details

Telephone:6351679790
Email: editor@ijirt.org
Website: ijirt.org

Policies