Handwritten Character and Digit Recognition with Deep Convolutional Neural Networks: A Comparative Study

Chui En Mook*, Chin Poo Lee, Kian Ming Lim, Jit Yan Lim

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)

Abstract

Handwritten character or digit recognition involves automatically classifying handwritten characters or digits from images. Previous studies focused on specific datasets and did not thoroughly compare different CNN architectures. This paper addresses these limitations by presenting a comparative study of six popular CNN architectures (VGG16, Xception, ResNet152V2, InceptionResNetV2, MobileNetV2, and DenseNet201) on three diverse datasets: English Handwritten Characters, Handwritten Digits, and MNIST. The experimental results demonstrate that the InceptionResNetV2 model with data augmentation achieves the highest accuracy across all datasets, with accuracies of 93.26%, 97.16%, and 99.71% on the English Handwritten Characters, Handwritten Digits, and MNIST datasets, respectively.

Original languageEnglish
Title of host publication2023 11th International Conference on Information and Communication Technology, ICoICT 2023
Pages137-141
Number of pages5
ISBN (Electronic)9798350321982
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event11th International Conference on Information and Communication Technology, ICoICT 2023 - Melaka, Malaysia
Duration: 23 Aug 202324 Aug 2023

Publication series

Name2023 11th International Conference on Information and Communication Technology, ICoICT 2023
Volume2023-August

Conference

Conference11th International Conference on Information and Communication Technology, ICoICT 2023
Country/TerritoryMalaysia
CityMelaka
Period23/08/2324/08/23

Keywords

  • Convolution Neural Network
  • Data Augmentation
  • Handwritten Character Recognition
  • Handwritten Digit Recognition

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