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A Cost-Efficient Framework for Scene Text Detection in the Wild

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Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 13031))

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Abstract

Scene text detection in the wild is a hot research area in the field of computer vision, which has achieved great progress with the aid of deep learning. However, training deep text detection models needs large amounts of annotations such as bounding boxes and quadrangles, which is laborious and expensive. Although synthetic data is easier to acquire, the model trained on this data has large performance gap with that trained on real data because of domain shift. To address this problem, we propose a novel two-stage framework for cost-efficient scene text detection. Specifically, in order to unleash the power of synthetic data, we design an unsupervised domain adaptation scheme consisting of Entropy-aware Global Transfer (EGT) and Text Region Transfer (TRT) to pre-train the model. Furthermore, we utilize minimal actively annotated and enhanced pseudo labeled real samples to fine-tune the model, aiming at saving the annotation cost. In this framework, both the diversity of the synthetic data and the reality of the unlabeled real data are fully exploited. Extensive experiments on various benchmarks show that the proposed framework significantly outperforms the baseline, and achieves desirable performance with even a few labeled real datasets.

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Acknowledgments

This work is supported by the Open Research Project of the State Key Laboratory of Media Convergence and Communication, Communication University of China, China (No. SKLMCC2020KF004), the Beijing Municipal Science & Technology Commission (Z191100007119002), the Key Research Program of Frontier Sciences, CAS, Grant NO ZDBS-LY-7024, and the National Natural Science Foundation of China (No. 62006221).

Author information

Authors and Affiliations

  1. Communication University of China, Beijing, China

    Gangyan Zeng & Yuan Zhang

  2. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Yu Zhou & Xiaomeng Yang

  3. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Yu Zhou & Xiaomeng Yang

Authors
  1. Gangyan Zeng

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  2. Yuan Zhang

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  3. Yu Zhou

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  4. Xiaomeng Yang

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Corresponding author

Correspondence toYu Zhou.

Editor information

Editors and Affiliations

  1. MIMOS Berhad, Kuala Lumpur, Malaysia

    Duc Nghia Pham

  2. Sirindhorn International Institute of Science and Technology, Thammasat University, Mueang Pathum Thani, Thailand

    Thanaruk Theeramunkong

  3. Data61, CSIRO, Brisbane, QLD, Australia

    Guido Governatori

  4. Department of Philosophy, Tsinghua University, Beijing, China

    Fenrong Liu

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Zeng, G., Zhang, Y., Zhou, Y., Yang, X. (2021). A Cost-Efficient Framework for Scene Text Detection in the Wild. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13031. Springer, Cham. https://doi.org/10.1007/978-3-030-89188-6_11

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