基于ResNet网络的东巴象形文字识别研究

Research on Dongba hieroglyph recognition using ResNet network

东巴象形文字是古代纳西族创造的文字,是世界文明的瑰宝。针对东巴象形文字传播的局限性,提出了基于图像处理和深度学习识别东巴文字的方法。文章通过构造恒等残差块和卷积残差块来搭建20层ResNet模型,采用随机梯度下降算法反向调整下一轮迭代的卷积层权值,经过训练自动得到图像相关特征参数并进行识别。实验结果表明,该算法识别东巴文字的平均准确率达93.58%,具有较高的识别精度,取得了较好的识别效果,本研究可为东巴文字的保护工作提供参考和方法支持。

Dongba hieroglyph,created by the ancient Naxi minority,is a treasure of world civilization.In view of the limitation of Dongba hieroglyph communication,a method of recognition of Dongba characters based on image processing and deep learning is proposed.In this paper,the 20-layer ResNet model is built by constructing the identity residual block and the convolution residual block,and the convolution layer weight of the next iteration is reversely adjusted by the stochastic gradient descent algorithm.After training,image related characteristic parameters are automatically obtained and identified.The experimental results show that the average accuracy of the algorithm in identifying Dongba characters is 93.58%,which has high recognition accuracy and achieves a good recognition effect.This study can provide reference and method support for the protection of Dongba hieroglyph.