Scene text removal via cascaded text stroke detection and erasing
Xuewei Bian, Chaoqun Wang, Weize Quan, Juntao Ye, Xiaopeng Zhang,, Dong-Ming Yan

TL;DR
This paper introduces a novel end-to-end framework for scene text removal that uses cascaded text stroke detection and erasing, significantly improving accuracy and visual quality over existing methods.
Contribution
The work presents a new cascaded approach with separate networks for text stroke detection and removal, and introduces a real-world dataset for better evaluation.
Findings
Outperforms state-of-the-art methods in text localization and erasing
Proposes a new real-world dataset for scene text removal
Demonstrates significant visual quality improvements
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…
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Multimodal Machine Learning Applications · Video Analysis and Summarization
