Summary of Ensemble Learning For Vietnamese Scene Text Spotting in Urban Environments, by Hieu Nguyen et al.
Ensemble Learning for Vietnamese Scene Text Spotting in Urban Environments
by Hieu Nguyen, Cong-Hoang Ta, Phuong-Thuy Le-Nguyen, Minh-Triet Tran, Trung-Nghia Le
First submitted to arxiv on: 1 Apr 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 proposes an efficient ensemble learning framework for Vietnamese scene text spotting. The approach combines multiple models to improve prediction accuracy, aiming to significantly enhance performance in challenging urban settings. Experimental evaluations on the VinText dataset show a significant improvement in accuracy compared to existing methods, with an impressive accuracy of 5%. The results demonstrate the efficacy of ensemble learning in Vietnamese scene text spotting and highlight its potential for real-world applications, such as text detection and recognition in urban signage, advertisements, and various text-rich scenes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to recognize words in Vietnamese street signs and ads. It combines multiple models to get better results. The team tested this approach on some data and it worked really well, with an accuracy of 5%. This shows that this method can be used in real-life situations to detect and read text in urban environments. |
