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

This paper contributes to research on handwritten OCR, cross-lingual OCR, multilingual AI, AutoML, neural architecture search, and LLM-driven model design by introducing a fully automated pipeline for handwritten optical character recognition across multiple scripts.

Unlike traditional OCR approaches that rely on manual neural architecture design, language-specific preprocessing, or expert-guided model selection, this work uses large language models to generate, evaluate, and refine OCR architectures in a closed-loop neural architecture search process.

The proposed framework applies GPT-5, GPT-4o, and Claude Sonnet 4 to automate model design for Arabic, English, and Persian handwritten text recognition. By testing the method across scripts with different visual and linguistic characteristics, the paper highlights the potential of LLM-driven AutoML for scalable multilingual OCR and low-resource language applications.

This work is relevant to researchers working on optical character recognition, handwritten text recognition, multilingual document intelligence, neural architecture search, AutoML, LLM agents, computer vision, and low-resource language processing.

Keywords: handwritten OCR, cross-lingual OCR, multilingual OCR, optical character recognition, handwritten text recognition, AutoML, neural architecture search, LLM-driven AutoML, large language models, GPT-5, GPT-4o, Claude Sonnet 4, LLM agents, automated model design, document intelligence, multilingual AI, Arabic handwriting recognition, Persian handwriting recognition, low-resource language processing, computer vision for text recognition.

Citation:

Kashaniyan, M., Ghassemi, A., & Mozayani, N. (2025). LLM-driven AutoML for cross-lingual handwritten OCR: Closed-loop neural architecture search with GPT-5, GPT-4o, and Claude Sonnet 4. In 2025 15th International Conference on Computer and Knowledge Engineering (ICCKE), IEEE, pp. 1–6. https://doi.org/10.1109/ICCKE68588.2025.11273810