OCR or Not? Rethinking Document Information Extraction in the MLLMs Era with Real-World Large-Scale Datasets
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910039192633344 |
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| author | Shen, Jiyuan Yuan, Peiyue Ghosh, Atin Mai, Yifan Dahlmeier, Daniel |
| author_facet | Shen, Jiyuan Yuan, Peiyue Ghosh, Atin Mai, Yifan Dahlmeier, Daniel |
| contents | 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 advancing document information extraction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02789 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | OCR or Not? Rethinking Document Information Extraction in the MLLMs Era with Real-World Large-Scale Datasets Shen, Jiyuan Yuan, Peiyue Ghosh, Atin Mai, Yifan Dahlmeier, Daniel Computation and Language Artificial Intelligence 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 advancing document information extraction. |
| title | OCR or Not? Rethinking Document Information Extraction in the MLLMs Era with Real-World Large-Scale Datasets |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2603.02789 |