Summary of Vision-language Model Based Handwriting Verification, by Mihir Chauhan et al.
Vision-Language Model Based Handwriting Verification
by Mihir Chauhan, Abhishek Satbhai, Mohammad Abuzar Hashemi, Mir Basheer Ali, Bina Ramamurthy, Mingchen Gao, Siwei Lyu, Sargur Srihari
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 investigates the application of Vision Language Models (VLMs) in handwriting verification, a crucial task in document forensics. The authors aim to address concerns from forensic document examiners about the lack of explainability and reliance on extensive training data in deep learning-based approaches. By leveraging VLMs’ Visual Question Answering capabilities and 0-shot Chain-of-Thought reasoning, the researchers seek to provide human-understandable explanations for model decisions. Experiments on the CEDAR handwriting dataset show that VLMs offer enhanced interpretability, reduce the need for large training datasets, and adapt better to diverse handwriting styles. However, results indicate that specialized deep learning models, such as CNN-based ResNet-18 architecture, outperform VLMs in terms of accuracy (84%). The study highlights the potential of VLMs in generating human-interpretable decisions while emphasizing the need for further advancements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores new ways to help computers recognize handwriting. Right now, computer programs can only explain their decisions if they’ve been trained on a lot of data and have special features designed by humans. This paper looks at how we can use special models called Vision Language Models (VLMs) to make these explanations more understandable for humans. The authors tested these models on a handwriting dataset and found that they can provide clear explanations, reduce the need for lots of training data, and adapt well to different writing styles. However, other approaches like CNN-based ResNet-18 architecture are still better at recognizing handwriting. |
Keywords
» Artificial intelligence » Cnn » Deep learning » Question answering » Resnet
