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arXiv · 1912.07183

MTRNet++: One-stage Mask-based Scene Text Eraser

Osman Tursun·Simon Denman·Rui Zeng·Sabesan Sivapalan·Sridha Sridharan·Clinton Fookes
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Abstract

A precise, controllable, interpretable and easily trainable text removal approach is necessary for both user-specific and large-scale text removal applications. To achieve this, we propose a one-stage mask-based text inpainting network, MTRNet++. It has a novel architecture that includes mask-refine, coarse-inpainting and fine-inpainting branches, and attention blocks. With this architecture, MTRNet++ can remove text either with or without an external mask. It achieves state-of-the-art results on both the Oxford and SCUT datasets without using external ground-truth masks. The results of ablation studies demonstrate that the proposed multi-branch architecture with attention blocks is effective and essential. It also demonstrates controllability and interpretability.

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Osman Tursun, Simon Denman, Rui Zeng, Sabesan Sivapalan, Sridha Sridharan, Clinton Fookes. 2019-12-16. MTRNet++: One-stage Mask-based Scene Text Eraser. https://doi.org/10.1016/j.cviu.2020.103066

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