Synthetic images generation for text detection and recognition in the wild

Natalia Khanzhina; Natalia Slepkova; Andrey Filchenkov

doi:10.1117/12.2557064

31 January 2020 Synthetic images generation for text detection and recognition in the wild

Natalia Khanzhina, Natalia Slepkova, Andrey Filchenkov

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 1143312 (2020) https://doi.org/10.1117/12.2557064
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

Deep neural networks help solving different images related tasks very efficiently, though their cost is high whereas a lot of data are required for training. While there is a great demand to build neural network models for optical character detection and recognition for different languages, such as, for mobile real-time applications, datasets collecting and labeling are quite expensive. In this paper, we propose the fully automated approach for synthetic images with text generation based on deep learning and projective geometry methods. For evaluation, we trained two neural networks on the dataset generated by our algorithm. Our approach enables to decrease the false negative rate on real images from SVT and SVT-50 datasets in comparison with training on SynthText dataset, giving ~1% of F₁-measure increasing.

Citation Download Citation

Natalia Khanzhina, Natalia Slepkova, and Andrey Filchenkov "Synthetic images generation for text detection and recognition in the wild", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 1143312 (31 January 2020); https://doi.org/10.1117/12.2557064

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