基于Mask R-CNN的甲骨文拓片的自动检测与识别研究

Automatic Detection and Recognition of Oracle Rubbings Based on Mask R-CNN

【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.