Scene text removal via cascaded text stroke detection and erasing
- URL: http://arxiv.org/abs/2011.09768v1
- Date: Thu, 19 Nov 2020 11:05:13 GMT
- Title: Scene text removal via cascaded text stroke detection and erasing
- Authors: Xuewei Bian, Chaoqun Wang, Weize Quan, Juntao Ye, Xiaopeng Zhang,
Dong-Ming Yan
- Abstract summary: Recent learning-based approaches show promising performance improvement for scene text removal task.
We propose a novel "end-to-end" framework based on accurate text stroke detection.
- Score: 19.306751704904705
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent learning-based approaches show promising performance improvement for
scene text removal task. However, these methods usually leave some remnants of
text and obtain visually unpleasant results. In this work, we propose a novel
"end-to-end" framework based on accurate text stroke detection. Specifically,
we decouple the text removal problem into text stroke detection and stroke
removal. We design a text stroke detection network and a text removal
generation network to solve these two sub-problems separately. Then, we combine
these two networks as a processing unit, and cascade this unit to obtain the
final model for text removal. Experimental results demonstrate that the
proposed method significantly outperforms the state-of-the-art approaches for
locating and erasing scene text. Since current publicly available datasets are
all synthetic and cannot properly measure the performance of different methods,
we therefore construct a new real-world dataset, which will be released to
facilitate the relevant research.
Related papers
- Leveraging Structure Knowledge and Deep Models for the Detection of Abnormal Handwritten Text [19.05500901000957]
We propose a two-stage detection algorithm that combines structure knowledge and deep models for handwritten text.
A shape regression network trained by a novel semi-supervised contrast training strategy is introduced and the positional relationship between the characters is fully employed.
Experiments on two handwritten text datasets show that the proposed method can greatly improve the detection performance.
arXiv Detail & Related papers (2024-10-15T14:57:10Z) - DeepEraser: Deep Iterative Context Mining for Generic Text Eraser [103.39279154750172]
DeepEraser is a recurrent architecture that erases the text in an image via iterative operations.
DeepEraser is notably compact with only 1.4M parameters and trained in an end-to-end manner.
arXiv Detail & Related papers (2024-02-29T12:39:04Z) - Enhancing Scene Text Detectors with Realistic Text Image Synthesis Using
Diffusion Models [63.99110667987318]
We present DiffText, a pipeline that seamlessly blends foreground text with the background's intrinsic features.
With fewer text instances, our produced text images consistently surpass other synthetic data in aiding text detectors.
arXiv Detail & Related papers (2023-11-28T06:51:28Z) - TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision [61.186488081379]
We propose TextFormer, a query-based end-to-end text spotter with Transformer architecture.
TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling.
It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing.
arXiv Detail & Related papers (2023-06-06T03:37:41Z) - Progressive Scene Text Erasing with Self-Supervision [7.118419154170154]
Scene text erasing seeks to erase text contents from scene images.
Current state-of-the-art text erasing models are trained on large-scale synthetic data.
We employ self-supervision for feature representation on unlabeled real-world scene text images.
arXiv Detail & Related papers (2022-07-23T09:05:13Z) - Towards End-to-End Unified Scene Text Detection and Layout Analysis [60.68100769639923]
We introduce the task of unified scene text detection and layout analysis.
The first hierarchical scene text dataset is introduced to enable this novel research task.
We also propose a novel method that is able to simultaneously detect scene text and form text clusters in a unified way.
arXiv Detail & Related papers (2022-03-28T23:35:45Z) - SwinTextSpotter: Scene Text Spotting via Better Synergy between Text
Detection and Text Recognition [73.61592015908353]
We propose a new end-to-end scene text spotting framework termed SwinTextSpotter.
Using a transformer with dynamic head as the detector, we unify the two tasks with a novel Recognition Conversion mechanism.
The design results in a concise framework that requires neither additional rectification module nor character-level annotation.
arXiv Detail & Related papers (2022-03-19T01:14:42Z) - Stroke-Based Scene Text Erasing Using Synthetic Data [0.0]
Scene text erasing can replace text regions with reasonable content in natural images.
The lack of a large-scale real-world scene-text removal dataset allows the existing methods to not work in full strength.
We enhance and make full use of the synthetic text and consequently train our model only on the dataset generated by the improved synthetic text engine.
This model can partially erase text instances in a scene image with a bounding box provided or work with an existing scene text detector for automatic scene text erasing.
arXiv Detail & Related papers (2021-04-23T09:29:41Z) - Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting [49.768327669098674]
We propose an end-to-end trainable text spotting approach named Text Perceptron.
It first employs an efficient segmentation-based text detector that learns the latent text reading order and boundary information.
Then a novel Shape Transform Module (abbr. STM) is designed to transform the detected feature regions into regular morphologies.
arXiv Detail & Related papers (2020-02-17T08:07:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.