Donut
An end-to-end document understanding model that operates without OCR, extracting structured information directly from document images without relying on traditional OCR preprocessing steps.
Timeline (last 90 days)
Currently mainstream open-weight vision/text models include Meta's Llama series, Mistral, Qwen-VL, LLaVA, and Donut and LayoutLMv3, which specialize in document understanding; their weights can be loaded through runtimes such as llama.cpp, Ollama, and Transformers, and they work offline.
The OCR route commonly uses tools such as Tesseract and PaddleOCR to first obtain plain text and then hand it to a language model to determine the fields; end-to-end vision models (such as Donut and Qwen-VL) skip explicit OCR and predict structured output directly from the image.
All Facts (3)
LayoutLM and Donut are representative specialized document understanding models.
60%UnverifiedCurrently mainstream open-weight vision/text models include Meta's Llama series, Mistral, Qwen-VL, LLaVA, and Donut and LayoutLMv3, which specialize in document understanding; their weights can be loaded through runtimes such as llama.cpp, Ollama, and Transformers, and they work offline.
50%UnverifiedThe OCR route commonly uses tools such as Tesseract and PaddleOCR to first obtain plain text and then hand it to a language model to determine the fields; end-to-end vision models (such as Donut and Qwen-VL) skip explicit OCR and predict structured output directly from the image.
50%