Classification of handwritten Chinese numbers with convolutional neural networks

Ameri, R, Alameer, A ORCID: https://orcid.org/0000-0002-7969-3609, Ferdowski, S, Abolghasemi, V and Nazarpour, K 2021, Classification of handwritten Chinese numbers with convolutional neural networks , in: 5th International Conference on Pattern Recognition and Image Analysis (IPRIA).

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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.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: 2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA)
Publisher: IEEE
ISBN: 9781665426596
Depositing User: A Alameer
Date Deposited: 26 May 2022 12:34
Last Modified: 13 Jun 2022 11:55
URI: http://usir.salford.ac.uk/id/eprint/63693

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