Multilingual OCR-Aware Fine-Tuning and Prompt-Guided Chain-of-Thought Reasoning for Multimodal Large Language Models
arXiv:2605.16409v2 Announce Type: replace-cross Abstract: Optical character recognition (OCR) and multilingual text understanding remain major failure modes of multimodal large language models (MLLMs), particularly in real-world images containing cluttered layouts, small fonts, blur, occlusion, and complex typography. We present an OCR-aware multilingual multimodal training framework that combines (i) large-scale synthetic OCR-to-translation data generation, (ii) OCR-aware supervised fine-tuning (SFT) with LoRA adaptation, and (iii) structured visual chain-of-thought (CoT) prompting for reason
Improvement in Multimodal Large Language Models (MLLMs) is a continuous and pressing area of AI research, with OCR and multilingual capabilities representing significant current limitations for broader real-world application.
This development addresses a critical vulnerability in MLLMs, allowing them to process and understand complex real-world text content more effectively, which is essential for numerous practical applications across various industries.
MLLMs will become significantly more capable of handling diverse text in images, reducing current failure rates and expanding their utility beyond clean, digital text, particularly in multilingual contexts and challenging visual environments.
- · AI developers
- · Multilingual tech companies
- · Document digitization services
- · Automation software providers
- · Legacy OCR software
- · Manual data entry services
Enhanced MLLMs will improve the accuracy and efficiency of information extraction from unstructured visual data.
This will accelerate automation in sectors like legal, finance, and logistics that heavily rely on document processing and international communication.
Improved multilingual OCR and reasoning could lead to the development of more sophisticated AI agents capable of understanding and interacting with global visual information at scale, potentially impacting cross-cultural intelligence gathering and automated translation.
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