一种基于端点顺序预测的手写体笔画恢复方法

Handwritten Drawing Order Recovery Method Based on Endpoint Sequential Prediction

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。

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.