Key Laboratory for Special Functional Materials of Ministry of Education, and School of Materials Science and Engineering, Henan University, Kaifeng, China.
Key Laboratory of Oracle Bone Inscriptions Information Processing, Ministry of Education of China, Anyang Normal University, Anyang, China.
PLoS One. 2022 Aug 19;17(8):e0272974. doi: 10.1371/journal.pone.0272974. eCollection 2022.
Oracle bone inscriptions (OBIs) are ancient Chinese scripts originated in the Shang Dynasty of China, and now less than half of the existing OBIs are well deciphered. To date, interpreting OBIs mainly relies on professional historians using the rules of OBIs evolution, and the remaining part of the oracle's deciphering work is stuck in a bottleneck period. Here, we systematically analyze the evolution process of oracle characters by using the Siamese network in Few-shot learning (FSL). We first establish a dataset containing Chinese characters which have finished a relatively complete evolution, including images in five periods: oracle bone inscriptions, bronze inscriptions, seal inscriptions, official script, and regular script. Then, we compare the performance of three typical algorithms, VGG16, ResNet, and AlexNet respectively, as the backbone feature extraction network of the Siamese network. The results show that the highest F1 value of 83.3% and the highest recognition accuracy of 82.67% are obtained by the combination of VGG16 and Siamese network. Based on the analysis, the typical structural performance of each period is evaluated and we identified that the optimized Siamese network is feasible to study the evolution of the OBIs. Our findings provide a new approach for oracle's deciphering further.
甲骨文是中国商代起源的古代汉字，现在只有不到一半的现存甲骨文得到了很好的破译。迄今为止，解读甲骨文主要依赖于专业历史学家利用甲骨文演变的规则，而甲骨文破译工作的其余部分则陷入了瓶颈期。在这里，我们通过在少样本学习（FSL）中使用暹罗网络系统地分析甲骨文的演变过程。我们首先建立了一个包含已经完成相对完整演变的汉字的数据集，这些汉字包括五个时期的图像：甲骨文、金文、篆书、隶书和楷书。然后，我们分别比较了 VGG16、ResNet 和 AlexNet 这三种典型算法作为暹罗网络的骨干特征提取网络的性能。结果表明，VGG16 和暹罗网络的组合得到了 83.3%的最高 F1 值和 82.67%的最高识别准确率。基于分析，我们评估了每个时期的典型结构性能，并确定了优化的暹罗网络是研究甲骨文演变的可行方法。我们的研究结果为甲骨文的进一步解读提供了一种新方法。
