基于改进DenseNet网络的书法字体识别算法

Calligraphic Font Recognition Algorithm Based on Improved DenseNet Network

汉字书法是中华传统文化的代表,但是,由于书法字体具有风格迥异、结构复杂、变形繁多等特点,给大众学习和欣赏书法带来了极大障碍.为了解决普通老百姓解读书法作品的困难,提出一种基于改进DenseNet网络的书法字体识别算法,设计区域权值比例池化规则替换传统DenseNet网络的最大池化和平均池化规则,采用Nadam算法优化模型训练效果,进行自适应学习率调整,此外,提出基于剪枝技术的模型裁剪策略,在保证识别性能的同时,提高了模型的训练效率.实验结果表明,在由楷书、行书、隶书和篆书4类字体组成的混合字体数据集中,本文算法获得了96.13%的识别率,优于另外5种深度学习模型.

Chinese calligraphy is one of the representatives of Chinese traditional culture.However,the different styles,complex structures,and various distortions of calligraphic fonts have brought great obstacles to learning and appreciating calligraphy for the public.A calligraphic font recognition algorithm based on an improved DenseNet network is proposed to solve the difficulty of ordinary people in interpreting calligraphy works.A regional weight ratio pooling rule is designed to replace the maximum pooling and average pooling rules of the traditional DenseNet network.The Nadam algorithm is used to adjust the adaptive learning rate and optimize the model training effect.In addition,a model pruning strategy based on the pruning technology is proposed,which ensures a strong recognition performance and improves the training efficiency of the model.The experimental results show that in a mixed font data set composed of four types of fonts,namely the standard script,the running script,the clerical script,and the seal script,the proposed algorithm obtains a recognition rate of 96.13%,which is better than those of the other five deep learning models.