Summary of Spatial Context-based Self-supervised Learning For Handwritten Text Recognition, by Carlos Penarrubia et al.
Spatial Context-based Self-Supervised Learning for Handwritten Text Recognition
by Carlos Penarrubia, Carlos Garrido-Munoz, Jose J. Valero-Mas, Jorge Calvo-Zaragoza
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 The proposed research investigates the application of Self-Supervised Learning (SSL) methodologies, specifically Spatial Context-based SSL, to Handwritten Text Recognition (HTR). The authors aim to adapt and optimize these approaches for HTR and propose new workflows that leverage the unique features of handwritten text. The study demonstrates advancements in the state-of-the-art of SSL for HTR using various benchmark cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Handwritten Text Recognition is a challenging problem that requires contextualizing handwritten texts. While Self-Supervised Learning has been successful in computer vision, its application to HTR has been limited. This research explores how Spatial Context-based Self-Supervised Learning can be used for HTR and proposes new workflows. The study shows that this approach leads to better results than previous methods. |
Keywords
» Artificial intelligence » Self supervised
