Summary of Attention Based End to End Network For Offline Writer Identification on Word Level Data, by Vineet Kumar et al.
Attention based End to end network for Offline Writer Identification on Word level data
by Vineet Kumar, Suresh Sundaram
First submitted to arxiv on: 11 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 A novel paper presents improvements to writer identification algorithms, which have shown promising results in various fields despite limitations when faced with limited or incomplete handwriting samples. The authors highlight the need for more accurate identification methods, especially in scenarios where only partial or fragmented handwritten data is available. By leveraging word images and exploring techniques that capitalize on the availability of a small number of handwriting samples, the researchers aim to enhance writer identification accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new study aims to improve how well computers can recognize who wrote something by looking at a limited amount of handwriting. This can be useful in many areas, such as checking signatures or identifying writers of documents. Right now, these algorithms work well when they have plenty of handwriting to look at, but they struggle when there’s not much to go on. The researchers want to make it easier for computers to figure out who wrote something even when all they have is a few words. |
