Summary of Graph Neural Network Based Handwritten Trajectories Recognition, by Anuj Sharma et al.
Graph Neural Network based Handwritten Trajectories Recognition
by Anuj Sharma, Sukhdeep Singh, S Ratna
First submitted to arxiv on: 15 May 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 The novel combination of handwritten trajectory features as chain codes and graph neural networks shows promising results for both offline and online handwriting recognition tasks. By leveraging the strengths of each technique, the paper achieves state-of-the-art performance with minimal error rates and rapid convergence. This breakthrough has significant implications for real-world applications, particularly in areas where handwritten input is a critical component. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Handwriting recognition is an important area that requires both offline and online capabilities. A new approach combines chain codes and graph neural networks to achieve impressive results. This technique uses chain codes to extract features from handwritten trajectories, which are then fed into graph neural networks for processing. The authors claim this combination outperforms previous methods and reduces error rates quickly. |
