How LLM-Driven AutoML Can Improve Cross-Lingual Handwritten OCR

Thu, 02 Jul 2026 00:00:00 +0000

Handwritten text recognition remains a challenging problem in machine learning, especially when models must work across multiple languages and writing systems. Arabic, English, and Persian scripts each introduce different visual structures, character shapes, stroke patterns, and recognition challenges. These differences make cross-lingual handwritten OCR difficult to scale using traditional manually designed models.

In our paper, “LLM-Driven AutoML for Cross-Lingual Handwritten OCR: Closed-Loop Neural Architecture Search with GPT-5, GPT-4o, and Claude Sonnet 4,” we study how large language models can support fully automated model design for handwritten optical character recognition.

The main idea is to use LLMs as AutoML agents. Instead of relying on manual neural architecture design, domain-specific preprocessing, or human model selection, the proposed pipeline allows large language models to generate, evaluate, and refine OCR architectures in a closed-loop neural architecture search process.

The framework uses GPT-5, GPT-4o, and Claude Sonnet 4 to independently propose and improve neural network architectures for handwritten OCR. The approach is evaluated on Arabic, English, and Persian handwritten text recognition, making it relevant to multilingual OCR, low-resource language processing, document intelligence, and automated machine learning.

This work contributes to research on LLM-driven AutoML, cross-lingual OCR, handwritten text recognition, neural architecture search, multilingual AI, and automated model design.

Researchers working on OCR, AutoML, LLM agents, computer vision, Persian handwriting recognition, Arabic handwriting recognition, or low-resource language technologies may find this work useful.

Paper

LLM-Driven AutoML for Cross-Lingual Handwritten OCR: Closed-Loop Neural Architecture Search with GPT-5, GPT-4o, and Claude Sonnet 4
Mobina Kashaniyan, Amirhossein Ghassemi, Nasser Mozayani
Published in 2025 15th International Conference on Computer and Knowledge Engineering (ICCKE), IEEE.

DOI: https://doi.org/10.1109/ICCKE68588.2025.11273810

PDF : https://mobinakashaniyan.github.io/papers/llm-driven-automl-cross-lingual-handwritten-ocr.pdf

IEEE Xplore: https://ieeexplore.ieee.org/abstract/document/11273810

Keywords

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

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

Tue, 28 Oct 2025 00:00:00 +0000

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

Handwritten OCR | Mobina Kashaniyan

How LLM-Driven AutoML Can Improve Cross-Lingual Handwritten OCR

Paper

Keywords

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