Practical Guide on Document Understanding: From OCR to VLM

Project page (Google Scholar indexing):https://yafehlis.github.io/document-understanding-paper/ The evolution from optical character recognition (OCR) to modern vision-language models (VLMs) represents a fundamental shift in how machines understand documents. This paper provides a practical framework for navigating this landscape, moving beyond simple taxonomies to characterize models along five capability dimensions: text recognition, structural understanding, output flexibility, spatial awareness, and task adaptability. We trace the architectural lineage from convolutional neural networks (CNNs) through Vision Transformers (ViT) to contemporary multimodal systems, revealing two distinct evolutionary paths: one optimizing for pixel-level precision, another for semantic reasoning. Through analysis of real-world applications, particularly complex forms with non-textual elements like checkboxes, we illustrate how architectural choices fundamentally determine what problems models can solve. Our framework enables practitioners to match model capabilities to application requirements by explicitly mapping the tradeoff space of accuracy, cost, latency, and flexibility.