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OCR or Not? Rethinking Document Information Extraction in the MLLMs Era with Real-World Large-Scale Datasets

arXiv – CS AI|Jiyuan Shen, Peiyue Yuan, Atin Ghosh, Yifan Mai, Daniel Dahlmeier|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

A large-scale benchmarking study finds that powerful Multimodal Large Language Models (MLLMs) can extract information from business documents using image-only input, potentially eliminating the need for traditional OCR preprocessing. The research demonstrates that well-designed prompts and instructions can further enhance MLLM performance in document processing tasks.

Key Takeaways

→MLLMs can achieve comparable performance to OCR-enhanced approaches when processing documents with image-only input.
→Traditional OCR preprocessing may not be necessary for powerful MLLMs in document information extraction tasks.
→Carefully designed schema, exemplars, and instructions can significantly enhance MLLM performance.
→The study used an automated hierarchical error analysis framework leveraging LLMs to systematically diagnose error patterns.
→The research provides practical guidance for advancing document information extraction using MLLMs.

#mllm #document-extraction #ocr #multimodal #large-language-models #ai-research #nlp #benchmarking #automation #machine-learning

Read Original →via arXiv – CS AI

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