TY - GEN
T1 - Classification of Handwritten Chinese Numbers with Convolutional Neural Networks
AU - Amen, Rasoul
AU - Alameer, Ali
AU - Ferdowsi, Saideh
AU - Abolghasem, Vahid
AU - Nazarpour, Kianoush
PY - 2021/7/26
Y1 - 2021/7/26
N2 - 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.
AB - 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.
U2 - 10.1109/ipria53572.2021.9483557
DO - 10.1109/ipria53572.2021.9483557
M3 - Conference contribution
SN - 9781665426596
T3 - International Conference on Pattern Recognition and Image Analysis (IPRIA): Proceedings
BT - 2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA)
ER -