Summary of A Comprehensive Survey Of Oracle Character Recognition: Challenges, Benchmarks, and Beyond, by Jing Li et al.
A comprehensive survey of oracle character recognition: challenges, benchmarks, and beyond
by Jing Li, Xueke Chi, Qiufeng Wang, Dahan Wang, Kaizhu Huang, Yongge Liu, Cheng-lin Liu
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper presents a comprehensive survey of research on oracle character recognition (OrCR), which is crucial for understanding ancient Chinese inscriptions found on oracle bones. OrCR is an interdisciplinary field that combines archaeology, paleography, and historical cultural studies. Traditional manual methods are labor-intensive and limit accessibility to the general public. With recent advancements in pattern recognition and deep learning, there is a growing trend towards automating OrCR. The paper reviews key challenges, benchmark datasets, digital resources, research methodologies, and applications across diverse disciplines. It concludes by proposing future investigation avenues for significant advancements in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Oracle character recognition-an old technique that’s getting new life! Imagine being able to read ancient Chinese inscriptions without needing experts. That’s what this paper is all about. Right now, it takes a lot of time and expertise to understand these ancient scripts. But with computers getting smarter, people are working on ways to automate the process. This paper looks at all the different approaches that have been tried so far, what works well, and where there’s still room for improvement. It also talks about how this technology could be used in other fields, like archaeology or history. |
Keywords
» Artificial intelligence » Deep learning » Pattern recognition
