Ameri, Rasoul and Alameer, Ali and Ferdowsi, Saideh and Abolghasemi, Vahid and Nazarpour, Kianoush (2021) Classification of Handwritten Chinese Numbers with Convolutional Neural Networks. In: 2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA), 2021-04-28 - 2021-04-29, Kashan, Iran.
Ameri, Rasoul and Alameer, Ali and Ferdowsi, Saideh and Abolghasemi, Vahid and Nazarpour, Kianoush (2021) Classification of Handwritten Chinese Numbers with Convolutional Neural Networks. In: 2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA), 2021-04-28 - 2021-04-29, Kashan, Iran.
Ameri, Rasoul and Alameer, Ali and Ferdowsi, Saideh and Abolghasemi, Vahid and Nazarpour, Kianoush (2021) Classification of Handwritten Chinese Numbers with Convolutional Neural Networks. In: 2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA), 2021-04-28 - 2021-04-29, Kashan, Iran.
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 (Paper) |
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Uncontrolled Keywords: | Chinese number classification; Convolutional neural network; Deep learning; Image processing; hand written recognition |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of Faculty of Science and Health > Mathematics, Statistics and Actuarial Science, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 30 Jun 2025 13:56 |
Last Modified: | 30 Jun 2025 14:03 |
URI: | http://repository.essex.ac.uk/id/eprint/36290 |
Available files
Filename: accepted manuscript.pdf