Drawing Order Recovery based on deep learning

Abstract

Humans have the ability to recover the order from static handwritten images, after a large amount of data training, the machine may learn some patterns in the training data to imitate or learn a certain skill similar to humans. To overcome the problem of sequence recovery of static image strokes, this paper proposes a stroke recovery method based on deep convolutional neural network model. In the model training phase, by using the two-dimensional static handwritten image, the process of writing a font is convert into three channels includes strokes that have been written, possible positions of next strokes, and the completed font, and state of the input sample are quantified. In the recovery phase, the restored font is preprocessed to obtain the stroke segments of the font, and the trained model is used to evaluate the sequential combination of different stroke segments, so as to obtain the correct stroke order. With no more than one hundred of characters’ writing experiences, the proposed method performs robustly and competitively among multi-writer handwriting DOR tasks.