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

TL;DR

This work proposes an automated hierarchical error analysis framework that leverages large language models (LLMs) to diagnose error patterns systematically and suggests that OCR may not be necessary for powerful MLLMs, as image-only input can achieve comparable performance to OCR-enhanced approaches.

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 advancing document information extraction.

Figures (11)

• Figure 1: Example of a document page extracted using our OCR engine.
• Figure 2: An example of textual content extracted by our in-house OCR engine.
• Figure 3: Hierarchical Error Analysis Framework
• Figure 4: Performance comparison on various size models across different input types. The small shape (, , ) denotes the arithmetic mean across two different categories of dataset. $+$ is the F1-score in C1, while $\times$ is for C2.
• Figure 5: Error analysis results for three different input modalities.