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AI Research
arXiv CS.AI·16d ago·5 min read
LLM-Driven AutoML for Cross-Lingual Handwritten OCR: Closed-Loop Neural Architecture Search with GPT-5, GPT-4o, and Claude Sonnet 4
TL;DR
Cross-lingual handwritten optical character recognition (OCR) faces challenges due to language diversity. This study introduces a closed-loop neural architecture search using GPT-5, GPT-4o, and Claude Sonnet 4 to automate model selection and optimization.
✦ Why It Matters
Engineers can implement LLM-driven AutoML frameworks to optimize OCR systems for multilingual applications today.
Key Takeaways
- Explore LLMs for automating neural architecture design in your projects.
- Utilize the framework for efficient cross-lingual OCR applications.
- Leverage high accuracy models without manual hyperparameter tuning.
- Implement findings to streamline OCR system development.
How It Works
The framework operates in a closed-loop manner, where LLMs autonomously generate neural network architectures. Each model is trained and evaluated, with performance metrics feeding back into the system to refine subsequent designs.
This iterative process allows for continuous improvement without human intervention, making it highly efficient.