一种基于端点顺序预测的手写体笔画恢复方法

计算机科学 ›› 2019, Vol. 46 ›› Issue (11A): 264-267.

一种基于端点顺序预测的手写体笔画恢复方法

张瑞¹, 湛永松², 杨明浩³

(桂林电子科技大学广西信息科学实验中心广西桂林541004)¹;
(桂林电子科技大学广西可信软件重点实验室广西桂林541004)²;
(中国科学院自动化研究所模式识别国家重点实验室北京100190)³

出版日期:2019-11-10 发布日期:2019-11-20
通讯作者: 湛永松(1979-),博士,教授,CCF会员,主要研究方向为多媒体技术,E-mail:yszhan@guet.edu。
作者简介:张瑞(1991-),男,硕士生,主要研究方向为图像处理、机器学习。
基金资助:
本文受广西自然科学基金项目(2017GXNSFAA198226),广西重点研发计划(AB17195027,AC16380124,AB18126053),桂林电子科技大学研究生创新教育项目(2018YJCX43)资助。

Handwritten Drawing Order Recovery Method Based on Endpoint Sequential Prediction

ZHANG Rui¹, ZHAN Yong-song², YANG Ming-hao³

(Guangxi Experiment Center of Information Science,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China)¹;
(Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China)²;
(The National Laboratory of Pattern Recognition,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China)³

Online:2019-11-10 Published:2019-11-20

摘要/Abstract

摘要： 针对汉字手写体的笔画动态序列恢复问题,文中提出了一种基于端点顺序预测的手写体笔画顺序恢复模型。首先对经过数字化处理后的手写体图像进行细化、笔画片段分割、图像坐标提取和规整等预处理,然后利用预处理后的图像和对应的书写坐标序列生成网络训练的样本,样本由静态手写体图像和包含字体书写顺序的热力图标签组成,该模型采用一种端到端的卷积神经网络结构,最后使用训练好的网络模型对静态手写体图像进行预测,从而得到字体原先的书写顺序。实验结果表明,该方法能够有效地对5笔以内的手写字体进行书写顺序的恢复,具有较高的准确率和处理速度。

关键词: 笔画恢复, 卷积神经网络, 深度学习, 时序信息, 手写字体

Abstract: To address the problem of dynamic sequential recovery for Chinese handwritten,a handwritten drawing order recovery model based on deep learning method was designed.First,the handwritten image is preprocessed by coordinate regularization,refinement,and interruption of intersections,then the preprocessed image and the corresponding written coordinate sequence are used to generate the sample of the network.The sample consists of a static handwritten image and a heat map label containing the font writing order.The model uses an end-to-end convolutional neural work.Finally,the trained network model is used to predict the static handwritten image to get the original writing order of the font.The experimental results show that the method can effectively recovery the drawing order of handwritten fonts that less than five strokes.

Key words: Convolutional neural networks, Deep learning, Handwriting, Order recovery, Time series information

中图分类号:

TP183

张瑞, 湛永松, 杨明浩. 一种基于端点顺序预测的手写体笔画恢复方法[J]. 计算机科学, 2019, 46(11A): 264-267. https://doi.org/

ZHANG Rui, ZHAN Yong-song, YANG Ming-hao. Handwritten Drawing Order Recovery Method Based on Endpoint Sequential Prediction[J]. Computer Science, 2019, 46(11A): 264-267. https://doi.org/

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