With the increasing adoption of Large Language Models (LLMs) and Vision-Language Models (VLMs),rich document analysis technologies for applications like Retrieval-Augmented Generation (RAG)and visual RAG are gaining significant attention.Recent research indicates that using VLMs yields better RAG performance,but processing rich documents remains a challenge since a single page contains large amounts of information.In this paper, we present SCAN (SemantiC Document Layout ANalysis),a novel approach that enhances both textual and visual Retrieval-Augmented Generation (RAG) systemsthat work with visually rich documents.It is a VLM-friendly approach that identifies document components with appropriate semantic granularity,balancing context preservation with processing efficiency.SCAN uses a coarse-grained semantic approach that divides documents into coherent regions covering contiguous components.We trained the SCAN model by fine-tuning object detection models on an annotated dataset.Our experimental results across English and Japanese datasets demonstrate that applying SCAN improvesend-to-end textual RAG performance by up to 9.4 points and visual RAG performance by up to 10.4 points,outperforming conventional approaches and even commercial document processing solutions.