[2603.02789] OCR or Not? Rethinking Document Information Extraction in the MLLMs Era with Real-World Large-Scale Datasets

Computer Science > Computation and Language arXiv:2603.02789 (cs) [Submitted on 3 Mar 2026] Title:OCR or Not? Rethinking Document Information Extraction in the MLLMs Era with Real-World Large-Scale Datasets Authors:Jiyuan Shen, Peiyue Yuan, Atin Ghosh, Yifan Mai, Daniel Dahlmeier View a PDF of the paper titled OCR or Not? Rethinking Document Information Extraction in the MLLMs Era with Real-World Large-Scale Datasets, by Jiyuan Shen and 4 other authors View PDF HTML (experimental) Abstract:Multimodal Large Language Models (MLLMs) enhance the potential of natural language processing. However, their actual impact on document information extraction remains unclear. In particular, it is unclear whether an MLLM-only pipeline--while simpler--can truly match the performance of traditional OCR+MLLM setups. In this paper, we conduct a large-scale benchmarking study that evaluates various out-of-the-box MLLMs on business-document information extraction. To examine and explore failure modes, we propose an automated hierarchical error analysis framework that leverages large language models (LLMs) to diagnose error patterns systematically. Our findings suggest that OCR may not be necessary for powerful MLLMs, as image-only input can achieve comparable performance to OCR-enhanced approaches. Moreover, we demonstrate that carefully designed schema, exemplars, and instructions can further enhance MLLMs performance. We hope this work can offer practical guidance and valuable insight for ad...