结合迁移引导和双向循环结构GAN的零样本文本识别

doi:10.16451/j.cnki.issn1003-6059.202012003

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Abstract

To improve the recognition accuracy of zero-shot recognition methods based on generative adversarial network(GAN),a zero-shot text recognition method combining transfer guidance and bidirectional cycle structure GAN is proposed.Bidirectional cycle structure GAN is constructed to improve the generation ability of the model,thus the generated pseudo features are closer to the real features of the input.The concept of transfer guided learning is introduced,and the model is trained by the transfer text instead of the seen text to improve the recognition accuracy of the unseen text.By adding an effective regularization term,the generator generates diverse results during the training process,and thus the stability of the generated model is improved.The experiment shows that the proposed method improves the accuracy of zero-shot recognition task with high generalization performance and it can be easily extended to other applications.

Key words： Transfer Guide Bidirectional Cycle Structure Zero-Shot Learning Text Recognition

Received: 22 July 2020

Fund:

National Natural Science Foundation of China(No.61462065)

Corresponding Authors: ZHANG Guimei,Ph.D.,professor.Her research interests include computer vision,image processing and pattern recognition.

About author:: LONG Bangyao,master student.His research interests include computer vision,image processing and pattern recognition;LU Feifei,master student.His research interests include computer vision,image processing and pattern recognition.

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	ZHANG Guimei
	LONG Bangyao
	LU Feifei

Cite this article:

ZHANG Guimei,LONG Bangyao,LU Feifei. Zero-Shot Text Recognition Combining Transfer Guide and Bidirectional Cycle Structure GAN[J]. , 2020, 33(12): 1083-1096.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202012003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I12/1083

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