一种基于端点顺序预测的手写体笔画恢复方法

Abstract

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

CLC Number:

TP183

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.

References

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Handwritten Drawing Order Recovery Method Based on Endpoint Sequential Prediction

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