基于深度学习的手写书法字体识别算法  

Handwritten Calligraphy Font Recognition Algorithm Based on Deep Learning

作　　者：许嘉谕 林楚烨 陈志涛 邓卓然 潘家辉 梁艳 XU Jia-Yu;LIN Chu-Ye;CHEN Zhi-Tao;DENG Zhuo-Ran;PAN Jia-Hui;LIANG Yan(School of Software,South China Normal University,Foshan 528225,China)

机构地区：[1]华南师范大学软件学院,佛山528225

出　　处：《计算机系统应用》2021年第2期213-218,共6页Computer Systems & Applications

基　　金：国家自然科学基金面上项目(61876067);广东省自然科学基金面上项目(2019A1515011375);广州市科技计划项目重点领域研发计划(202007030005)。

摘　　要：为解决因手写书法作品种类繁多而识别困难的问题,降低人们观赏书法的门槛,本文提出了基于深度学习的手写书法字体识别算法.识别过程中首先使用投影法等图像处理方法对书法作品图像中的汉字进行定位和分割,然后分别利用GoogLeNet Inception-v3模型和ResNet-50残差网络进行书体风格识别和字形识别.实验结果表明,本文算法能实现对整幅书法作品中楷书和篆书的书体风格以及字形的识别,对楷书和篆书单字的识别率分别为91.57%和81.70%,达到了实用的需求.In order to solve the problem of difficult recognition due to the large variety of handwritten calligraphy fonts and reduce the threshold for people to appreciate calligraphy,we propose a handwritten calligraphy font recognition algorithm based on deep learning.In the process of recognition,image processing methods,such as projection method,are first used to locate and segment the Chinese characters in the calligraphy works.Then,the GoogLeNet Inception-v3 model and ResNet-50 residual network are used to recognize the styles and shapes of Chinese characters.Consequently,this algorithm can recognize the styles and shapes of regular script and seal script in an entire calligraphy work at singlecharacter recognition rates of 91.57%and 81.70%,demonstrating its practicability.

关 键 词：图像处理 卷积神经网络(CNN) 手写书法字体识别 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]