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).
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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