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
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781665426596
ISBN (Print)9781665426602
DOIs
Publication statusPublished - 26 Jul 2021
Event5th International Conference on Pattern Recognition and Image Analysis - 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
Abbreviated titleIPRIA 2021
Country/TerritoryIran, Islamic Republic of
CityKashan
Period28/04/2129/04/21

Keywords / Materials (for Non-textual outputs)

  • Chinese number classification
  • convolutional neural network
  • deep learning
  • hand written recognition
  • image processing

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