A Comparative Study of Different Deep Learning Model for Recognition of Handwriting Digits

Pronab Ghosh, Atqiya Abida Anjum, Asif Karim, Masum Shah Junayed, Md. Zahid Hasan, Khan Md. Hasib, Al Nahian Bin Emran

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedingspeer-review

Abstract

With the expansion of Artificial Neural Network (ANN), Deep Learning (DL) has brought interesting turn in the various fields of Artificial Intelligence (AI) by making it smarter and more efficient than what we had even in 10-2 years back. DL has been in use in various fields due to its versatility. Convolutional Neural Network (CNN) is at the major point of advancement that brings together the ANN and innovative DL techniques. In this research paper, we have contrived a multi-layer, fully connected neural network (NN) with 10 and 12 hidden layers for handwritten digits (HD) recognition. The testing is performed on the publicly attainable MNIST handwritten database. We selected 60,000 images from the MNIST database for training, and 10,000 images for testing. Our multi-layers ANN (10), ANN (12) and CNN are able to achieve an overall accuracy of 99.10%, 99. 34% and 99.70% respectively while determining digits using the MNIST handwriting dataset.
Original languageEnglish
Title of host publicationProceedings of the International Conference on IoT based Control Networks and Intelligent Systems (ICICNIS 2020)
Place of PublicationIndia
PublisherSSRN
Pages857-866
Number of pages10
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event2nd International Conference on IoT Based Control Networks & Intelligent Systems - Kottayam, India
Duration: 28 Jun 202129 Jun 2021
Conference number: 2
http://icicnis.com/2021/

Conference

Conference2nd International Conference on IoT Based Control Networks & Intelligent Systems
Abbreviated titleICICNIS 2020
Country/TerritoryIndia
CityKottayam
Period28/06/2129/06/21
Internet address

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