Abstract
For successful scene text recognition (STR) models, synthetic text image generators have alleviated the lack of annotated text images from the real world. Specifically, they generate multiple text images with diverse backgrounds, font styles, and text shapes and enable STR models to learn visual patterns that might not be accessible from manually annotated data. In this paper, we introduce a new synthetic text image generator, SynthTIGER, by analyzing techniques used for text image synthesis and integrating effective ones under a single algorithm. Moreover, we propose two techniques that alleviate the long-tail problem in length and character distributions of training data. In our experiments, SynthTIGER achieves better STR performance than the combination of synthetic datasets, MJSynth (MJ) and SynthText (ST). Our ablation study demonstrates the benefits of using sub-components of SynthTIGER and the guideline on generating synthetic text images for STR models. Our implementation is publicly available at https://github.com/clovaai/synthtiger.
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References
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)
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)
Hwang, W., et al.: Post-OCR parsing: building simple and robust parser via bio tagging. In: Workshop on Document Intelligence at NeurIPS 2019 (2019)
Hwang, W., Yim, J., Park, S., Yang, S., Seo, M.: Spatial dependency parsing for 2D document understanding. arXiv preprint arXiv:2005.00642 (2020)
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)
Karatzas, D., Gomez-Bigorda, L., et al.: ICDAR 2015 competition on robust reading. In: ICDAR, pp. 1156–1160 (2015)
Karatzas, D., et al.: ICDAR 2013 robust reading competition. In: ICDAR, pp. 1484–1493 (2013)
Liao, M., Song, B., Long, S., He, M., Yao, C., Bai, X.: SynthText3D: synthesizing scene text images from 3D virtual worlds. Science China Inf. Sci. 63(2), 1–14 (2020). https://doi.org/10.1007/s11432-019-2737-0
Limonova, E., Bezmaternykh, P., Nikolaev, D., Arlazarov, V.: Slant rectification in Russian passport OCR system using fast Hough transform. In: Ninth International Conference on Machine Vision (ICMV 2016), vol. 10341, p. 103410P. International Society for Optics and Photonics (2017)
Liu, X., Meng, G., Pan, C.: Scene text detection and recognition with advances in deep learning: a survey. Int. J. Doc. Anal. Recognit. (IJDAR) 22(2), 143–162 (2019). https://doi.org/10.1007/s10032-019-00320-5
Long, S., He, X., Yao, C.: Scene text detection and recognition: the deep learning era. Int. J. Comput. Vision 129(1), 161–184 (2021). https://doi.org/10.1007/s11263-020-01369-0
Long, S., Yao, C.: UnrealText: synthesizing realistic scene text images from the unreal world. arXiv preprint arXiv:2003.10608 (2020)
Lucas, S.M., Panaretos, A., Sosa, L., Tang, A., Wong, S., Young, R.: ICDAR 2003 robust reading competitions. In: ICDAR, pp. 682–687 (2003)
Mishra, A., Alahari, K., Jawahar, C.: Scene text recognition using higher order language priors. In: BMVC (2012)
Motahari, H., Duffy, N., Bennett, P., Bedrax-Weiss, T.: A report on the first workshop on document intelligence (DI) at NeurIPS 2019. ACM SIGKDD Explor. Newsl. 22(2), 8–11 (2021)
Patel, C., Shah, D., Patel, A.: Automatic number plate recognition system (ANPR): a survey. Int. J. Comput. Appl. 69(9) (2013)
Phan, T.Q., Shivakumara, P., Tian, S., Tan, C.L.: Recognizing text with perspective distortion in natural scenes. In: ICCV, pp. 569–576 (2013)
Risnumawan, A., Shivakumara, P., Chan, C.S., Tan, C.L.: A robust arbitrary text detection system for natural scene images. ESWA 41, 8027–8048 (2014)
Shi, B., Yang, M., Wang, X., Lyu, P., Yao, C., Bai, X.: Aster: an attentional scene text recognizer with flexible rectification. IEEE Trans. Pattern Anal. Mach. Intell. 41(9), 2035–2048 (2018)
Simard, P.Y., Steinkraus, D., Platt, J.C., et al.: Best practices for convolutional neural networks applied to visual document analysis. In: ICDAR, vol. 3. Citeseer (2003)
Wang, K., Babenko, B., Belongie, S.: End-to-end scene text recognition. In: ICCV, pp. 1457–1464 (2011)
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Yim, M., Kim, Y., Cho, HC., Park, S. (2021). SynthTIGER: Synthetic Text Image GEneratoR Towards Better Text Recognition Models. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12824. Springer, Cham. https://doi.org/10.1007/978-3-030-86337-1_8
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DOI: https://doi.org/10.1007/978-3-030-86337-1_8
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