LLM-Driven AutoML for Cross-Lingual Handwritten OCR: Closed-Loop Neural Architecture Search with GPT-5, GPT-4o, and Claude Sonnet 4

2026-07-16 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision & Pattern Recognition · Depth: Expert, quick

Summary

A novel LLM-driven AutoML framework has been developed for cross-lingual handwritten optical character recognition (OCR). This system utilizes GPT-5, GPT-4o, and Claude Sonnet 4 as autonomous neural architecture designers. Each large language model independently generates, trains, evaluates, and iteratively refines neural network architectures based on performance feedback. The framework was rigorously evaluated across 270 independent experiments using Arabic, Persian, and English handwriting datasets. It consistently discovered accurate and computationally efficient models without requiring manual architecture design, domain-specific preprocessing, or hyperparameter tuning. The generated models achieved mean test accuracies exceeding 93 percent, with a peak accuracy of 98.1 percent, and maintained inference latencies between 41 and 44 milliseconds. This demonstrates the efficacy of large language models as AutoML agents for neural architecture search, facilitating scalable and reproducible handwriting recognition across diverse languages.

Key takeaway

For Machine Learning Engineers developing cross-lingual OCR solutions, you should consider integrating LLM-driven AutoML. This approach eliminates manual architecture design and hyperparameter tuning, significantly accelerating model development. Your team can achieve high accuracy and computational efficiency, as demonstrated by models reaching 98.1 percent accuracy with 41-44 ms inference latency. Explore using models like GPT-4o or Claude Sonnet 4 to autonomously discover and refine neural architectures for your specific script-adaptive recognition needs.

Key insights

LLMs can autonomously design, train, and refine neural architectures for complex tasks like cross-lingual OCR, achieving high accuracy and efficiency.

Principles

• LLMs can serve as effective AutoML agents.
• Iterative feedback drives architecture refinement.
• Automated NAS eliminates manual design.

Method

A closed-loop AutoML framework where GPT-5, GPT-4o, and Claude Sonnet 4 iteratively generate, train, evaluate, and refine neural network architectures using performance feedback.

In practice

• Automate neural architecture search with LLMs.
• Apply LLM-driven NAS to cross-lingual OCR.
• Eliminate manual hyperparameter tuning.

Topics

• LLM-Driven AutoML
• Neural Architecture Search
• Cross-Lingual OCR
• Handwritten Recognition
• GPT-5
• Claude Sonnet 4

Best for: Research Scientist, NLP Engineer, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.