摘要
【目的】将深度学习算法应用于甲骨文拓片的自动检测与识别中,助力传统文化的研究与普及。【方法】针对甲骨文拓片的图像特点创建数据集,在Mask R-CNN算法基础上,使用三元组损失函数和旋转角度回归技术进行优化,提高甲骨文字分类的准确性。【结果】对于训练数据集,甲骨文字符召回率为82%,检测和识别准确率均可达到95%,能够满足项目预计的技术指标要求。【局限】在文字残缺严重或漫漶等情境下,算法性能有待提升。【结论】模型具备实际使用价值,可进一步完善并推广应用。
[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.
作者
刘芳
李华飙
马晋
闫升
金沛然
Liu Fang;Li Huabiao;Ma Jin;Yan Sheng;Jin Peiran(National Museum of China,Beijing 100006,China;National Science Library,Chinese Academy of Sciences,Beijing 100190,China;Department of Library,Information and Archives Management,School of Economics and Management,University of Chinese Academy of Sciences,Beijing 100190,China;Key Laboratory of Collection Resources Revitalising Technology,Ministry of Culture and Tourism,Beijing 100006,China;Tianjin Hengda Wenbo S&T Co.,Ltd,Tianjin 300384,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2021年第12期88-97,共10页
Data Analysis and Knowledge Discovery
基金
文化艺术和旅游研究项目信息化发展专项项目(项目编号:MCT2020XZ12)的研究成果之一。