Summary of Self-supervised Learning Based Handwriting Verification, by Mihir Chauhan et al.
Self-Supervised Learning Based Handwriting Verification
by Mihir Chauhan, Mohammad Abuzar Hashemi, Abhishek Satbhai, Mir Basheer Ali, Bina Ramamurthy, Mingchen Gao, Siwei Lyu, Sargur Srihari
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 presents SSL-HV, a self-supervised learning framework applied to handwriting verification. The task involves determining whether two handwritten images come from the same or different writer distribution. The authors compare various generative and contrastive self-supervised learning approaches against handcrafted feature extractors and supervised learning on the CEDAR AND dataset. They find that a ResNet-based Variational Auto-Encoder (VAE) outperforms other generative methods, achieving 76.3% accuracy. Meanwhile, a ResNet-18 fine-tuned using Variance-Invariance-Covariance Regularization (VICReg) surpasses other contrastive approaches with 78% accuracy. Furthermore, the authors demonstrate that pre-trained VAE and VICReg models improve the downstream task of writer verification by 6.7% and 9%, respectively, compared to a supervised baseline with 10% writer labels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using machine learning to tell if two handwritten notes were written by the same person or not. The researchers tested different ways of doing this using artificial intelligence and found that one method was better than others. They also showed that these AI methods could be used to improve how well we can identify who wrote a piece of handwriting. This is important because it could help with tasks like verifying signatures and identifying fake documents. |
Keywords
» Artificial intelligence » Encoder » Machine learning » Regularization » Resnet » Self supervised » Supervised
