Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (3): 182-188.DOI: 10.3778/j.issn.1002-8331.1911-0117

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Trans-Net:Stick Figure Recognition Based on Transfer Learning

YAO Kexin, CAO Weiqun   

  1. School of Information Science and Technology, Beijing Forestry University, Beijing 100087, China
  • Online:2021-02-01 Published:2021-01-29



  1. 北京林业大学 信息学院,北京 100087


Handwritten input can transfer rich text information through a small amount of writing. How to accurately recognize hand written stick figure has attracted more and more attention of researchers from all walks of life. The traditional recognition algorithm of stick figure is based on the relatively fixed structure characteristics of stick figure. This method has a high recognition accuracy for simple stick figures with clear handwriting and relatively simple structures. However, with the increase of the object of classifications and the complexity of the structure of the stick figure, such methods have some limitations, which often cause misclassification. In order to obtain a better recognition effect, this paper takes stick figure with a corresponding template as the research object. The image generation algorithm is used to preprocess the handwriting input, and a simple recognition model(Trans-Net) based on convolutional neural network is proposed to recognize. Particularly, the use of transfer learning solves the deficient of dataset in the sample library. Experimental results show that the proposed method can effectively extract the features of the handwriting input, and the average recognition accuracy of 150 types of stick figure objects in the sample library reaches 94.1%.

Key words: stick figure recognition, convolutional neural network, figure generation, transfer learning



关键词: 简笔画识别, 卷积神经网络, 图像生成, 迁移学习