The classification of materials of oracle bone is one of the most basic aspects for oracle bone morphology. However, the classification method depending on experts' experience requires long-term learning and accumulation for professional knowledge. This article presents a multiregional convolutional neural network to classify the rubbings of oracle bones. First, we detected the “shield grain” and “tooth grain” on the oracle bone rubbings, then complete the division of multiple areas on an image of oracle bone. Second, the convolutional neural network is used to extract the features of each region and we complete the fusion of multiple local features. Finally, the classification of tortoise shell and animal bone was realized. Utilizing the image of oracle bone provided by experts, we conducted an experiment; the result show our method has better classification accuracy. It has made contributions to the progress of the study of oracle bone morphology.

中文翻译：

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一种基于局部卷积神经网络框架的Oracle材料分类方法

甲骨材料的分类是甲骨形态最基本的方面之一。但是，依靠专家经验的分类方法需要专业知识的长期学习和积累。本文提出了一个多区域卷积神经网络来对甲骨的拓片进行分类。首先，我们在甲骨拓片上检测到“盾纹”和“齿纹”，然后在甲骨图像上完成多个区域的划分。其次，利用卷积神经网络提取每个区域的特征，完成多个局部特征的融合。最终实现了龟甲和兽骨的分类。利用专家提供的甲骨图像，我们进行了实验；结果表明我们的方法具有更好的分类精度。为甲骨形态研究的进展做出了贡献。