# MSLKANet: A Multi-Scale Large Kernel Attention Network for Scene Text Removal

arXiv: https://arxiv.org/abs/2211.06565 · HTML: https://mlsanity.com/paper/2211.06565/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 34 references that are in the map.
- First posted on arXiv 12 Nov 2022.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Character Region Awareness for Text Detection](https://mlsanity.com/paper/1904.01941/) (2019)
- [Visual Attention Network](https://mlsanity.com/paper/2202.09741/) (2022)
- [Editing Text in the Wild](https://mlsanity.com/paper/1908.03047/) (2019)
- [MTRNet: A Generic Scene Text Eraser](https://mlsanity.com/paper/1903.04092/) (2019)
- [EnsNet: Ensconce Text in the Wild](https://mlsanity.com/paper/1812.00723/) (2018)
- [Scene Text Eraser](https://mlsanity.com/paper/1705.02772/) (2017)
- [MTRNet++: One-stage Mask-based Scene Text Eraser](https://mlsanity.com/paper/1912.07183/) (2019)
- [Scene text removal via cascaded text stroke detection and erasing](https://mlsanity.com/paper/2011.09768/) (2020)
- [Scene Text Retrieval via Joint Text Detection and Similarity Learning](https://mlsanity.com/paper/2104.01552/) (2021)
- [PERT: A Progressively Region-based Network for Scene Text Removal](https://mlsanity.com/paper/2106.13029/) (2021)
- [STRIVE: Scene Text Replacement In Videos](https://mlsanity.com/paper/2109.02762/) (2021)

## Abstract (arXiv)

Scene text removal aims to remove the text and fill the regions with perceptually plausible background information in natural images. It has attracted increasing attention due to its various applications in privacy protection, scene text retrieval, and text editing. With the development of deep learning, the previous methods have achieved significant improvements. However, most of the existing methods seem to ignore the large perceptive fields and global information. The pioneer method can get significant improvements by only changing training data from the cropped image to the full image. In this paper, we present a single-stage multi-scale network MSLKANet for scene text removal in full images. For obtaining large perceptive fields and global information, we propose multi-scale large kernel attention (MSLKA) to obtain long-range dependencies between the text regions and the backgrounds at various granularity levels. Furthermore, we combine the large kernel decomposition mechanism and atrous spatial pyramid pooling to build a large kernel spatial pyramid pooling (LKSPP), which can perceive more valid pixels in the spatial dimension while maintaining large receptive fields and low cost of computation. Extensive experimental results indicate that the proposed method achieves state-of-the-art performance on both synthetic and real-world datasets and the effectiveness of the proposed components MSLKA and LKSPP.