Overview

Document understanding studies how systems read and reason over documents such as forms, reports, historical manuscripts, and technical drawings, enabling search, extraction, and analysis at scale. Approaches combine OCR and layout analysis with vision-language models, specialized document models, retrieval-augmented generation, and agents that gather and verify evidence. Systems can perform well on focused tasks, but complex layouts, handwriting, long documents, and cross-page reasoning remain difficult; benchmarks such as DocVQA and real-world form and long-document evaluations expose these gaps. A central tension is how to balance general-purpose multimodal models with specialized OCR and structured pipelines that can be more reliable, efficient, and auditable.

Latest papers

CardsList

93 papers6 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

  1. A Multi-Stage Framework for Kuzushiji Character Recognition in Japanese Historical Documents

    Feb 22, 2026Rui-Yang Ju, Kohei Yamashita, Hirotaka Kameko +1Optical Character RecognitionHistorical Document Processing

  2. Context-Aware Mapping of 2D Drawing Annotations to 3D CAD Features Using LLM-Assisted Reasoning for Manufacturing Automation

    Feb 20, 2026Muhammad Tayyab Khan, Lequn Chen, Wenhe Feng +1Industrial AutomationManufacturing