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- W4385990845 abstract "Text erasure from an image is helpful for various tasks such as image editing and privacy preservation. We present TPFNet, a novel one-stage network for text removal from images. TPFNet has two parts: feature synthesis and image generation. Since noise can be more effectively removed from low-resolution images, part 1 operates on low-resolution images. Part 1 uses PVT or EfficientNet-B6 as the encoder. Further, we use a novel multi-headed decoder that generates a high-pass filtered image and a segmentation map, along with a text-free image. The segmentation branch helps locate the text precisely, and the high-pass branch helps in learning the image structure. Part 2 uses the features learned in part 1 to predict a high-resolution text-free image. To precisely locate the text, TPFNet employs an adversarial loss that is conditional on the segmentation map rather than the input image. On Oxford, SCUT, SCUT-EnsText and ICDAR2013 datasets, TPFNet outperforms recent networks on nearly all the metrics. E.g., on Oxford dataset, TPFNet has a PSNR (higher is better) of 44.2 and a text-detection precision (lower is better) of 39.0, compared to MTRNet++’s PSNR of 33.7 and precision of 50.4. The source code can be obtained from https://github.com/CandleLabAI/TPFNet ." @default.
- W4385990845 created "2023-08-19" @default.
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- W4385990845 date "2023-01-01" @default.
- W4385990845 modified "2023-10-14" @default.
- W4385990845 title "TPFNet: A Novel Text In-painting Transformer for Text Removal" @default.
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- W4385990845 doi "https://doi.org/10.1007/978-3-031-41731-3_10" @default.
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