TY - GEN
T1 - Handwritten Character and Digit Recognition with Deep Convolutional Neural Networks: A Comparative Study
AU - Mook, Chui En
AU - Poo Lee, Chin
AU - Lim, Kian Ming
AU - Yan Lim, Jit
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Convolution Neural Network
KW - Data Augmentation
KW - Handwritten Character Recognition
KW - Handwritten Digit Recognition
UR - http://www.scopus.com/inward/record.url?scp=85174394586&partnerID=8YFLogxK
U2 - 10.1109/ICoICT58202.2023.10262721
DO - 10.1109/ICoICT58202.2023.10262721
M3 - Conference Proceeding
AN - SCOPUS:85174394586
T3 - 2023 11th International Conference on Information and Communication Technology, ICoICT 2023
SP - 137
EP - 141
BT - 2023 11th International Conference on Information and Communication Technology, ICoICT 2023
T2 - 11th International Conference on Information and Communication Technology, ICoICT 2023
Y2 - 23 August 2023 through 24 August 2023
ER -