Multimodal Approaches for Visually-Rich Document Type Classification: A Comparative Analysis
A new research paper analyzes multimodal approaches for classifying visually-rich documents, comparing transformer and LLM-based architectures. The study evaluated LayoutLMv3, Donut, Qwen3-VL-32B-Instruct, and Qwen3-32B on the RVL-CDIP benchmark. Results indicate that specialized multimodal Transformers are superior for documents with complex layouts, with image information being the most critical factor for classification. AI
IMPACT Provides guidance on selecting effective multimodal architectures and feature combinations for document classification tasks.