Summary of A Novel Framework For Text Detection From Natural Scene Images with Complex Background, by Basavaraj Kaladagi et al.
A Novel Framework For Text Detection From Natural Scene Images With Complex Background
by Basavaraj Kaladagi, Jagadeesh Pujari
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 A novel and efficient approach is proposed to detect text regions from camera images with complex backgrounds using Wavelet Transforms. The framework employs grayscale image processing followed by sub-band filtering and region clustering techniques to identify text areas. This generalized method outperforms previous approaches, as it does not rely on specific font sizes. Experimental results are demonstrated on a dataset of 50 images with varying backgrounds and edge prominence. The proposed method can be easily adapted for different applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Text recognition from camera images is challenging due to varied and complex backgrounds. A new method uses Wavelet Transforms to detect text regions efficiently. First, the image is converted to grayscale, then sub-band filtering and region clustering are applied. This approach identifies text areas without relying on specific font sizes. The method was tested on 50 images with different backgrounds and edge prominence. |
Keywords
» Artificial intelligence » Clustering
