Summary of Spanish Trocr: Leveraging Transfer Learning For Language Adaptation, by Filipe Lauar et al.
Spanish TrOCR: Leveraging Transfer Learning for Language Adaptation
by Filipe Lauar, Valentin Laurent
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 The study investigates the transfer learning capabilities of the TrOCR architecture to Spanish, leveraging its state-of-the-art performance in English benchmarks. It explores two approaches: integrating an English TrOCR encoder with a language-specific decoder or fine-tuning the English base model on new language data. To overcome the scarcity of publicly available datasets, the authors propose a resource-efficient pipeline for creating OCR datasets in any language and present a comprehensive benchmark of image generation methods focused on Visual Rich Documents (VRDs). The study demonstrates that fine-tuning the English TrOCR on Spanish yields superior recognition compared to the language-specific decoder for a fixed dataset size. The authors evaluate their model using character and word error rate metrics on a public printed dataset, comparing its performance with other open-source and cloud OCR Spanish models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how a special kind of AI called TrOCR can be used to recognize text in Spanish. This AI was originally great at recognizing English text, but the scientists wanted to see if it could also work well with Spanish text. They tried two different ways to make the AI work with Spanish: one way involved combining an English-based part of the AI with a new Spanish-specific part, and the other way involved teaching the entire AI system how to recognize Spanish text from scratch. The scientists also came up with a simple way to create more training data for the AI, which is useful because there isn’t much public data available in Spanish. They found that the second method worked better than the first one, and they compared their results to some other existing systems. |
Keywords
» Artificial intelligence » Decoder » Encoder » Fine tuning » Image generation » Transfer learning
