Classification of Handwritten Chinese Numbers with Convolutional Neural Networks

Rasoul Amen, Ali Alameer, Saideh Ferdowsi, Vahid Abolghasem, Kianoush Nazarpour

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

2 Citations (Scopus)

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 publication2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA)
Number of pages4
DOIs
Publication statusPublished - 26 Jul 2021

Publication series

NameInternational Conference on Pattern Recognition and Image Analysis (IPRIA): Proceedings
PublisherIEEE
ISSN (Electronic)2049-3630

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