Synthetic dataset generation for text recognition with generative adversarial networks

Valeria Efimova; Viacheslav Shalamov; Andrey Filchenkov

doi:10.1117/12.2558271

31 January 2020 Synthetic dataset generation for text recognition with generative adversarial networks

Valeria Efimova, Viacheslav Shalamov, Andrey Filchenkov

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 1143315 (2020) https://doi.org/10.1117/12.2558271
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

Automated text recognition is used in autonomous driving systems, search engines, document analysis, and many other applications. There are many techniques to extract text information from scanned documents, but text recognition from arbitrary images is a much harder task. Recently suggested deep learning approaches have demonstrated highquality results, but they require a huge amount of data to achieve them. The process of collecting and labelling training data to train a deep learning network is costly. In this paper, we suggest an approach for automatic dataset generation for text recognition for arbitrary languages. We use a generative adversarial network structure, which is adapted to generate readable and clear text looking naturally on the image background. We evaluate our approach using SegLink and Textboxes++ text localization models, which were trained on examples generated by SynthText and by variations of our method. The comparison showed the superiority of our method on a subset of the ICDAR 2017 dataset for English and Arabic languages.

Citation Download Citation

Valeria Efimova, Viacheslav Shalamov, and Andrey Filchenkov "Synthetic dataset generation for text recognition with generative adversarial networks", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 1143315 (31 January 2020); https://doi.org/10.1117/12.2558271

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