一种基于深度学习的青铜器铭文识别方法

A Deep Learning Based Method for Bronze Inscription Recognition

考古出土的青铜器铭文是非常宝贵的文字材料,准确、快速地了解其释义和字形演变源流对考古学、历史学和语言学研究均有重要意义.青铜器铭文的辨识需要综合文字的形、音、义进行研究,其中第一步也是最重要的一步就是分析文字的形体特征.本文提出一种基于两阶段特征映射的神经网络模型来提取每个文字的形体特征,最后对比目前已知的文字研究成果,如《古文字类编》、《说文解字》,得出识别的结果.通过定性和定量的实验分析,我们发现本文提出的方法可达到较高的识别精度.特别地,在前10个预测类别中(Top-10)准确率达到了94.2%,大幅缩小了考古研究者的搜索推测空间,提高了青铜铭文识别的效率和准确性.

Bronze inscriptions from archaeology are very valuable text materials. Accurate and rapid understanding of their meaning and shape evolution is important for archeology, history and linguistics. It is necessary to combine characters shape, phonology and meaning for recognition of bronze inscription, wherein the first and also the most important step is to analyze shapes of bronze inscriptions. In this paper, we present a bronze inscription analysis method based on convolutional neural network（CNN） with two-phase feature mapping. We first extract the bronze inscriptions by image acquisition, and then, by comparing with the currently known character research results, e. g., ＂Ancient Chinese Character Type Series＂ and ＂Shuo Wen Jie Zi＂, we obtain the recognition results. Through qualitative and quantitative experimental analyses, we find that the proposed method achieves high recognition accuracy. Specifically, we achieve 94.2 % accuracy for the Top-10, greatly reducing the space of archaeological search and improving the efficiency and accuracy of bronze inscription recognition.