  Text erasure from an image is helpful for various tasks such as image editing
and privacy preservation. In this paper, we present TPFNet, a novel one-stage
(end-toend) network for text removal from images. Our network 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.
The output of part 1 is a low-resolution text-free image. Part 2 uses the
features learned in part 1 to predict a high-resolution text-free image. In
part 1, we use "pyramidal vision transformer" (PVT) as the encoder. Further, we
use a novel multi-headed decoder that generates a high-pass filtered image and
a segmentation map, in addition to a text-free image. The segmentation branch
helps locate the text precisely, and the high-pass branch helps in learning the
image structure. 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, and SCUT-EnsText datasets, our network outperforms recently
proposed networks on nearly all the metrics. For example, on SCUT-EnsText
dataset, TPFNet has a PSNR (higher is better) of 39.0 and text-detection
precision (lower is better) of 21.1, compared to the best previous technique,
which has a PSNR of 32.3 and precision of 53.2. The source code can be obtained
from https://github.com/CandleLabAI/TPFNet
