An Improved Neural Network Model Based on Inception-v3 for Oracle Bone Inscription Character Recognition
Ziyi Guo, Zihan Zhou, Bingshuai Liu, Longquan Li, Qingju Jiao, Chenxi Huang, Jianwei Zhang
Abstract
Oracle bone inscription is the ancestor of modern Chinese characters. Character recognition is an essential part of the research of oracle bone inscription. In this paper, we propose an improved neural network model based on Inception-v3 for oracle bone inscription character recognition. We replace the original convolution block and add the Contextual Transformer block and the Convolutional Block Attention Module. We conduct character recognition experiments with the improved model on two oracle bone inscription character image datasets, HWOBC and OBC306, and the results indicate that the improved model can still achieve excellent results in the cases of blurred, occluded, and mutilated characters. We also select AlexNet, VGG-19, and Inception-v3 neural network models for the same experiments, and the comparison result shows that the proposed model outperforms other models in three evaluation indicators, namely, Top-1 Accuracy, Top-3 Accuracy, and Top-5 Accuracy, which indicate the correctness and excellence of our proposed model.