Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (3): 509-521.doi: 10.1007/s11390-019-1923-y

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

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A Large Chinese Text Dataset in the Wild

Tai-Ling Yuan1, Zhe Zhu2, Kun Xu1, Member, CCF, IEEE, Cheng-Jun Li3, Tai-Jiang Mu1,*, Member, CCF, Shi-Min Hu1, Fellow, CCF, Senior Member, IEEE   

  1. 1 Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;
    2 Department of Radiology, Duke University, North Carolina 27708, U.S.A.;
    3 Tencent Technology (Beijing) Co. Ltd., Beijing 100080, China
  • Received:2018-12-24 Revised:2019-03-20 Online:2019-05-05 Published:2019-05-06
  • Contact: Tai-Jiang Mu E-mail:mmmutj@gmail.com
  • About author:Tai-Ling Yuan is a Ph.D. candidate in the Department of Computer Science and Technology, Tsinghua University, Beijing. He received his B.S. degree in computer science and technology from the same university in 2016. His research interests include computer graphics and computer vision.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China under Grant Nos. 61822204 and 61521002, a research grant from the Beijing Higher Institution Engineering Research Center, and the Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology.

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In this paper, we introduce a very large Chinese text dataset in the wild. While optical character recognition (OCR) in document images is well studied and many commercial tools are available, the detection and recognition of text in natural images is still a challenging problem, especially for some more complicated character sets such as Chinese text. Lack of training data has always been a problem, especially for deep learning methods which require massive training data. In this paper, we provide details of a newly created dataset of Chinese text with about 1 million Chinese characters from 3 850 unique ones annotated by experts in over 30 000 street view images. This is a challenging dataset with good diversity containing planar text, raised text, text under poor illumination, distant text, partially occluded text, etc. For each character, the annotation includes its underlying character, bounding box, and six attributes. The attributes indicate the character's background complexity, appearance, style, etc. Besides the dataset, we give baseline results using state-of-the-art methods for three tasks:character recognition (top-1 accuracy of 80.5%), character detection (AP of 70.9%), and text line detection (AED of 22.1). The dataset, source code, and trained models are publicly available.

Key words: Chinese text dataset; Chinese text detection; Chinese text recognition;

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