Multimodal Approaches for Visually-Rich Document Type Classification: A Comparative Analysis

Researchers conducted a systematic comparison of multimodal document classification approaches, evaluating transformer-based models (LayoutLMv3, Donut) against large language models (Qwen3-VL, Qwen3) on the RVL-CDIP benchmark. The study demonstrates that specialized multimodal transformers outperform LLM-based approaches for visually rich documents, with image data proving more critical than OCR-extracted text.

This research addresses a fundamental challenge in document processing: how to effectively combine textual, visual, and layout information for accurate classification. The study's primary contribution lies in establishing a unified experimental framework that enables fair comparison across heterogeneous architectural approaches—a significant gap in the field where previous evaluations used inconsistent methodologies.

The document classification problem has intensified as organizations increasingly handle diverse document types across multiple formats. Traditional rule-based systems struggle with visual complexity, while early deep learning approaches treated modalities independently. The emergence of multimodal transformers and vision-language models has created competing paradigms, but practitioners lacked clear guidance on architecture selection. This research systematically deconstructs those choices.

The findings carry practical implications for document processing pipelines. Organizations building document management systems must choose between specialized multimodal transformers and general-purpose LLMs. The evidence suggesting image information dominates classification signals suggests visual feature extraction quality directly impacts system performance. The secondary role of OCR-derived text is particularly significant—it indicates that high-quality OCR engines may provide diminishing returns compared to investments in robust image processing.

For technology teams, these results suggest that layout-intensive document types warrant specialized transformer architectures rather than deploying larger, more costly LLM solutions. The framework itself enables future researchers to conduct controlled comparisons as new models emerge, addressing the reproducibility challenges that plague multimodal AI research. Organizations should prioritize image preprocessing and feature engineering while treating OCR as a complementary signal rather than primary information source.

• →Specialized multimodal transformers outperform general-purpose LLMs on visually rich document classification tasks
• →Image information provides the strongest signal for accurate document type classification, while OCR text plays a secondary supporting role
• →A unified evaluation framework enables systematic comparison across heterogeneous multimodal architectures and their design strategies
• →Layout structure in documents makes multimodal processing essential—single-modality approaches fundamentally underperform
• →OCR-free approaches show competitive performance, suggesting OCR quality optimization may provide diminishing returns