Summary of Visual Text Meets Low-level Vision: a Comprehensive Survey on Visual Text Processing, by Yan Shu et al.
Visual Text Meets Low-level Vision: A Comprehensive Survey on Visual Text Processing
by Yan Shu, Weichao Zeng, Zhenhang Li, Fangmin Zhao, Yu Zhou
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a comprehensive review of recent advancements in visual text processing, a crucial aspect of computer vision. The study focuses on leveraging unique textual characteristics to improve visual text detection, recognition, and manipulation. It introduces a hierarchical taxonomy of areas such as image enhancement, restoration, and manipulation, as well as different learning paradigms. The paper also explores the integration of textual features like structure, stroke, semantics, style, and spatial context into various tasks. Benchmarking is conducted on several widely-used datasets. The authors aim to establish this survey as a fundamental resource for future research in visual text processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Visual text is an important part of computer vision that helps us understand documents and scenes better. Researchers have made progress in detecting, recognizing, and manipulating visual text using special models. However, there are still challenges because text has unique properties that make it different from other objects. This paper reviews what’s been done so far in this field. It groups the research into areas like enhancing and restoring images, and manipulating them too. The authors also talk about how to use text features like its structure, strokes, meaning, style, and position to improve results. They test some methods on famous datasets and discuss the challenges ahead. |
Keywords
* Artificial intelligence * Semantics
