基于Capsule网络的甲骨文构件识别方法

Recognition of Oracle Radical based on the Capsule network

甲骨文作为中国最早的成形文字系统,具有重要的文化和学术价值。研究甲骨文构件和其构形系统是破译未识别的甲骨文的重要方向,但是甲骨文构件的标记工作只能由资深专家来完成,并且需要耗费大量时间和精力。针对这些问题,提出了一种基于Capsule网络和迁移学习的模型OracleNet,可以自动识别并标记甲骨文字形中包含的构件;同时,构建了包含标记的甲骨文字形和构件数据集,用于模型的训练和评估。实验结果显示,OracleNet模型对甲骨文构件的预测精确度达到了60%以上,其中Top5精确度达到了71.56%,验证了模型的有效性。

As the earliest shaped character system in China,the inscriptions on bones or tortoise shells of the Shang Dynasty(c.16th–11th century BC)have important cultural and academic values.The research on the constructional element,i.e.,Oracle Radical,and configuration system of the inscriptions on bones or tortoise shells of the Shang Dynasty is a vital direction to identify the unrecognized Oracle Graphics.However,the marking of Radicals can only be done by experienced experts;moreover,it will take a considerable amount of time and effort.To solve these problems,we proposed a model,i.e.,OracleNet,based on the Capsule network and transfer learning,which can automatically identify the Oracle Radical contained in a graphic.At the same time,we built a labeled Oracle Graphics dataset and a labeled Radicals dataset,which were used for training and evaluating the model.The experiment showed that the OracleNet had more than 60%precision for recognizing Radicals in a graphic and the Top 5 precision reached 71.56%,which verified validity of the model.