Automated Recognition of Oracle Bone Inscriptions Using Deep Learning and Data Augmentation

Oracle bone inscriptions (OBIs) are the earliest Chinese writing system. However, deciphering OBIs is a very challenging task because of the lack of data and time- and resource-consuming manual classification process. In this paper, I apply the technology of deep learning to solve the problem of OBI recognition, proposing a method for merging incompatible OBI classification datasets and implementing it successfully, significantly raising the training and testing accuracy of the neural networks tested. Another major contribution of this paper is the inclusion of a residual module on the AlexNet convolutional neural network, which achieves an accuracy of 89.51% after hyperparameter optimization on the merged dataset, about 1% better than the classical AlexNet under the same conditions and meets the expectation.