Classification of Handwritten Chinese Numbers with Convolutional Neural Networks

Rasoul Ameri, Ali Alameer, Saideh Ferdowsi, Vahid Abolghasemi, Kianoush Nazarpour

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Deep learning methods have become the key ingredient in the field of computer vision; in particular, convolutional neural networks (CNNs). Appropriating the network architecture and data pre-processing have significant impact on performance. This paper focuses on the classification of handwritten Chinese numbers. Firstly, we applied various methods of pre-processing to our collected image dataset. Secondly, we customised a CNN-based architecture with minimal number of layers and parameters specifically for the task. Experimental results showed that our proposed methods provides superior classification rate of 99.1%. Our results also show that the proposed method has competitive performance compared to smaller neural networks with fewer parameters, e.g. Squeezenet and deeper networks with a larger size and number of parameters, e.g., pre-trained GoogLeNet and MobileNetV2.

Original languageEnglish
Title of host publicationProceedings of the 5th International Conference on Pattern Recognition and Image Analysis, IPRIA 2021
PublisherIEEE
ISBN (Electronic)978-1-6654-2659-6
ISBN (Print)978-1-6654-2660-2
DOIs
Publication statusPublished - 26 Jul 2021
Event5th International Conference on Pattern Recognition and Image Analysis, IPRIA 2021 - Kashan, Iran, Islamic Republic of
Duration: 28 Apr 202129 Apr 2021

Publication series

NameProceedings of the 5th International Conference on Pattern Recognition and Image Analysis, IPRIA 2021
PublisherIEEE
ISSN (Electronic)2049-3630

Conference

Conference5th International Conference on Pattern Recognition and Image Analysis, IPRIA 2021
Country/TerritoryIran, Islamic Republic of
CityKashan
Period28/04/2129/04/21

Keywords

  • Chinese number classification
  • Convolutional neural network
  • Deep learning
  • hand written recognition
  • Image processing

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