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Weakly supervised scene text generation for low-resource languages

Published:2024-03 Issue: Volume:237 Page:121622
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Xie Yangchen,Chen Xinyuan^ORCID,Zhan Hongjian,Shivakumara Palaiahnakote,Yin Bing,Liu Cong,Lu Yue^ORCID

Funder

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference52 articles.

1. Atienza, R. (2021). Data augmentation for scene text recognition. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 1561–1570).

2. Baek, K., Choi, Y., Uh, Y., Yoo, J., & Shim, H. (2021). Rethinking the truly unsupervised image-to-image translation. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 14154–14163).

3. Baek, J., Kim, G., Lee, J., Park, S., Han, D., Yun, S., et al. (2019). What is wrong with scene text recognition model comparisons? dataset and model analysis. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 4715–4723).

4. Baek, J., Matsui, Y., & Aizawa, K. (2021). What if we only use real datasets for scene text recognition? toward scene text recognition with fewer labels. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 3113–3122).

5. Bautista, D., & Atienza, R. (2022). Scene text recognition with permuted autoregressive sequence models. In European conference on computer vision (pp. 178–196).

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