SynthTIGER: Synthetic Text Image GEneratoR Towards Better Text Recognition Models

Author:

Yim MoonbinORCID,Kim YoonsikORCID,Cho Han-CheolORCID,Park SungraeORCID

Publisher

Springer International Publishing

Reference21 articles.

1. Baek, J., et al.: What is wrong with scene text recognition model comparisons? Dataset and model analysis. In: International Conference on Computer Vision (ICCV) (2019)

2. Gupta, A., Vedaldi, A., Zisserman, A.: Synthetic data for text localisation in natural images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2315–2324 (2016)

3. Hwang, W., et al.: Post-OCR parsing: building simple and robust parser via bio tagging. In: Workshop on Document Intelligence at NeurIPS 2019 (2019)

4. Hwang, W., Yim, J., Park, S., Yang, S., Seo, M.: Spatial dependency parsing for 2D document understanding. arXiv preprint arXiv:2005.00642 (2020)

5. Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: Synthetic data and artificial neural networks for natural scene text recognition. In: Workshop on Deep Learning, NIPS (2014)

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