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
[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.
刘芳, 李华飙, 马晋, 闫升, 金沛然. 基于Mask R-CNN的甲骨文拓片的自动检测与识别研究*[J]. 数据分析与知识发现, 2021, 5(12): 88-97.
Liu Fang, Li Huabiao, Ma Jin, Yan Sheng, Jin Peiran. Automatic Detection and Recognition of Oracle Rubbings Based on Mask R-CNN. Data Analysis and Knowledge Discovery, 2021, 5(12): 88-97.
(National Museum of China. Studies of the Collections of the National Museum of China: Oracle [M]. Shanghai: Shanghai Ancient Books Publishing House, 2007: 292-293.)
(National Museum of China, Chinese Calligraphers Association. A Collection of Oracle and Bronze of National Museum of China[M]. Hefei: Anhui Fine Arts Publishing House, 2015: 5-8.)
(Lost Knowledge is Endless——Viewing the Research Progress of Oracle Inscriptions from the Topic Selection of the National Social Science Fund[EB/OL].[2021-04-09]. https://www.sohu.com/a/424326096_488440.)
(The Results of Special Commissioned Projects of the National Social Fund in 2018 [EB/OL]. [2021-04-09]. http://www.nopss.gov.cn/n1/2019/0703/c219507-31211146.html.)
(Xiong Jing, Han Shengwei. On the Importance of Cross-modal Knowledge Graph in Oracle Inscriptions Research[J]. Yindu Academic Journal, 2020, 41(3): 60-64, 97.)
Bogacz B, Gertz M, Mara H. Cuneiform Character Similarity Using Graph Representations [C]//Proceedings of the 20th Computer Vision Winter Workshop. 2015: 77-83.
[12]
Dencker T, Klinkisch P, Maul S M, et al. Deep Learning of Cuneiform Sign Detection with Weak Supervision Using Transliteration Alignment[J]. PLoS One, 2020, 15(12): e0243039.
doi: 10.1371/journal.pone.0243039
[13]
He K M, Gkioxari G, Dollár P, et al. Mask R-CNN[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(2): 386-397.
doi: 10.1109/TPAMI.34
(Guo Moruo. Oracle Collection[M]. Beijing: China Book Bureau, 1982.)
[17]
李宗焜. 甲骨文字编[M]. 北京: 中华书局, 2012.
[17]
(Li Zongkun. Oracle’s Words Made Up[M]. Beijing: China Book Bureau, 2012.)
[18]
刘钊, 冯克坚. 甲骨文常用字字典[M]. 北京: 中华书局, 2019.
[18]
(Liu Zhao, Feng Kejian. Oracle’s Common Word Dictionary[M]. Beijing: China Book Bureau, 2019.)
[19]
Gupta A, Vedaldi A, Zisserman A. Synthetic Data for Text Localisation in Natural Images [C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016: 2315-2324.
(Zhang Yikang, Zhang Heng, Liu Yongge, et al. Oracle Character Recognition Based on Cross-Modal Deep Metric Learning[J]. Acta Automatica Sinica, 2021, 47(4): 791-800.)
(Zhou Xinlun, Li Feng, Hua Xingcheng, et al. A Method of Jia Gu Wen Recognition Based on a Two-Level Classification[J]. Journal of Fudan University (Natural Science), 1996, 35(5): 481-486.)
(Lv Xiaoqing, Li Monan, Cai Kaiwei, et al. A Graphic-Based Method for Chinese Oracle-Bone Classification[J]. Journal of Beijing Information Science & Technology University, 2010, 25(S2): 92-96.)
(Lu Xuzheng, Cai Hengjin, Lin Li. Recognition of Oracle Radical Based on the Capsule Network[J]. CAAI Transactions on Intelligent Systems, 2020, 15(2): 243-254.)
[25]
邢济慈. 基于深度卷积神经网络的甲骨文字检测技术研究[D]. 郑州: 郑州大学, 2020.
[25]
(Xing Jici. Research of Oracle Bone Inscription Detection Based on Deep Convolutional Neural Network[D]. Zhengzhou: Zhengzhou University, 2020.)
(Xu Guiliang. Research on Oracle Bone Radical Detection Based in Deep Learning of Semantic Analysis[D]. Nanchang: Jiangxi Science and Technology Normal University, 2020.)
(Jiang Yiwei, Gu Xingsheng. Image Matching Algorithm Based on Grid Acceleration and Sequential Selection Strategy[J]. Journal of East China University of Science and Technology, DOI: 10.14135/j.cnki.1006-3080.20210401002.)
doi: 10.14135/j.cnki.1006-3080.20210401002