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| Automatic Detection and Recognition of Oracle Rubbings Based on Mask R-CNN |
Liu Fang1,2,3,Li Huabiao1,4( ),Ma Jin5,Yan Sheng5,Jin Peiran5 |
1National Museum of China, Beijing 100006, China
2National Science Library, Chinese Academy of Sciences, Beijing 100190, China
3Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
4Key Laboratory of Collection Resources Revitalising Technology, Ministry of Culture and Tourism, Beijing 100006, China
5Tianjin Hengda Wenbo S&T Co., Ltd, Tianjin 300384, China |
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Abstract [Objective] This paper applies the deep learning algorithm to automatically detect and recognize Oracle rubbings, aiming to improve the research and promotion of traditional culture. [Methods] Based on the Mask R-CNN algorithm, we used the three-tuple loss function and rotation angle regression technique to optimize and improve the accuracy of Oracle character classification. [Results] We examined our model with training datasets of Oracle Rubbing Images. The recall of Oracle characters reached 82%, and the detection and identification accuracy reached 95%, which met the expectations of the project. [Limitations] For the severe damaged or ambiguous texts, the performance of our new algorithm needs to be improved. [Conclusions] The proposed model has many practical values and could be further polished.
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Received: 29 June 2021
Published: 20 January 2022
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| Fund:Information Development Project of Cultural, Artistic and Tourism Research(MCT2020XZ12) |
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Corresponding Authors:
Li Huabiao,ORCID：0000-0003-4336-0287
E-mail: lihuabiao@chnmuseum.cn
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