Vision language models for end-to-end document parsing

Document parsing often relies on multi-stage pipelines combining layout detection, traditional OCR and post-processing scripts. These approaches often struggle with complex layouts and mathematical formulas. Vision language models (VLMs) for end-to-end document parsing simplify this architecture by treating the document image as a single input modality, preserving natural reading order and structured content. Open-source models specifically trained for this purpose — such as olmOCR-2, the token-efficient DeepSeek-OCR (3B) and the ultra-compact PaddleOCR-VL — have yielded highly efficient results. While VLMs reduce architectural complexity by replacing multi-stage pipelines, their generative nature makes them prone to hallucinations. Use cases with a low tolerance for error may still require a hybrid approach or deterministic OCR. Teams dealing with high-volume document ingestion should evaluate these unified approaches to determine whether they can replace complex legacy pipelines while maintaining accuracy and reducing long-term maintenance overhead.