[1]MAO Yafei,BI Xiaojun.Rubbing oracle bone character recognition based on improved ResNeSt network[J].CAAI Transactions on Intelligent Systems,2023,18(3):450-458.[doi:10.11992/tis.202211041]
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Rubbing oracle bone character recognition based on improved ResNeSt network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 3 Page number: 450-458 Column: 学术论文—机器学习 Public date: 2023-07-05
Title:
Rubbing oracle bone character recognition based on improved ResNeSt network
Author(s):
MAO Yafei1;  BI Xiaojun2
1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;
2. Department of Information Engineering, Minzu University of China, Beijing 100081, China
Keywords:
ResNeSt network model; oracle bone character recognition; jump connection; coordinate attention mechanism; optimization of classifier; OBC306; deep learning; neural network
CLC:
TP391
DOI:
10.11992/tis.202211041
Abstract:
At present, the methods for recognition of rubbing oracle bone characters have some problems, such as weak ability to extract local details and low recognition rate for some oracle characters with highly similarity. Therefore, an oracle bone character recognition method based on the improved ResNeSt network is proposed in this paper. By designing jump connection structure, the shallow network features are gradually transferred to the deep network and merged. At the same time, combined with the Strip characteristics of the oracle bone characters, the coordinate attention mechanism module is introduced to carry out the weighted fusion of the obtained features in both width and height directions. Finally, the network classifier is optimized effectively by removing the activation function of the last layer and the full connection layer, and resetting the number of output channels of the last convolution layer. The experimental results show that the recognition accuracy of the improved rubbing oracle bone character recognition model proposed in this paper reaches 93.53% on OBC306 data set, having achieved the best recognition effect at present.
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