Summary of On the Generalization Of Handwritten Text Recognition Models, by Carlos Garrido-munoz et al.
On the Generalization of Handwritten Text Recognition Models
by Carlos Garrido-Munoz, Jorge Calvo-Zaragoza
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 limitations of Handwritten Text Recognition (HTR) models when generalizing to out-of-distribution (OOD) data. Recent advances in HTR have focused on minimizing in-distribution (ID) errors, but real-world applications require models to generalize to OOD data without prior access. The authors analyze 336 OOD cases from eight state-of-the-art HTR models across seven datasets and five languages, finding that textual divergence between domains is the most significant factor for generalization, followed by visual divergence. They also show that the error of HTR models in OOD scenarios can be reliably estimated, with discrepancies falling below 10 points in 70% of cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well Handwritten Text Recognition (HTR) machines can recognize writing that’s different from what they were trained on. So far, researchers have focused on making HTR better for the same kind of writing, but this isn’t helpful for real-life situations where the writing might be different. The authors looked at many examples of OOD writing and found out why some HTR machines do better than others. They also figured out how to predict when an HTR machine will make mistakes with OOD writing. |
Keywords
» Artificial intelligence » Generalization
