VQ-STE: Scene text erasing with mask refinement and vector-quantized texture dictionary

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhengmi Tang , Tomo Miyazaki , Zhijie Wang , Yongsong Huang , Jonathan Pradana Mailoa , Shinichiro Omachi
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引用次数: 0

Abstract

Scene text erasing (STE), which aims to remove text from natural images and restore a plausible background, has been extensively researched in recent years. Most existing STE methods employ segment-then-erase pipelines, either explicitly or implicitly. However, these methods still face challenges, such as inaccurate text segmentation, difficulty in large text removal, and the shortage of training data. To address the first two issues, we present a scene-text-erasing network, VQ-STE, and to mitigate the third issue, we introduce a high-quality synthetic dataset, MixSyn. VQ-STE comprises a lightweight text Mask Refinement Network (MRN) and a Texture Dictionary-based Inpainting Network (TDIN). The MRN refines the bounding box-level text region mask, producing a high-recall stroke-level text mask by incorporating data augmentation and multiple loss functions. The TDIN erases large text regions by replacing distorted features with those from a pre-learned Texture Dictionary. Moreover, our generated MixSyn dataset offers greater diversity in background, text appearance, layout, and annotation compared to existing synthetic datasets. VQ-STE performs effectively in one or two-step settings, i.e., with or without additional text bounding box information. Experimental results demonstrate that VQ-STE outperforms existing one-step and two-step methods in quantitative and qualitative evaluations on the SCUT-EnsText dataset.
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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