MODEL PROFILE

NVIDIA-Nemotron-Parse-v1.2

NVIDIA · NVIDIA-Nemotron-Parse-v1.2

NVIDIA Nemotron Parse v1.2 is designed to understand document semantics and extract text and tables elements with spatial grounding. Given an image, NVIDIA Nemotron Parse v1.2 produces structured annotations, including formatted text, bounding-boxes and the corresponding semantic classes, ordered according to the document's reading flow. It overcomes the shortcomings of traditional OCR technologies that struggle with complex document layouts with structural variability, and helps transform unstructured documents into actionable and machine-usable representations. This has several downstream benefits such as increasing the availability of training-data for Large Language Models (LLMs), improving the accuracy of extractor, curator, retriever and AI agentic applications, and enhancing document understanding pipelines. NVIDIA-Nemotron-Parse v1.2 extends the prompt interface with a fourth prompt token category — **Text-in-picture prompts** — controlling whether the model extracts text from embedded images (e.g. photographs, figures): - ` `: extract text found inside pictures. - ` `: skip text extraction from pictures, reducing generation time by avoiding unnecessary tokens.

Visual QAlicense:other
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/11/1 11:34:38
OVERVIEW

Model overview

NVIDIA Nemotron Parse v1.2 is designed to understand document semantics and extract text and tables elements with spatial grounding. Given an image, NVIDIA Nemotron Parse v1.2 produces structured annotations, including formatted text, bounding-boxes and the corresponding semantic classes, ordered according to the document's reading flow. It overcomes the shortcomings of traditional OCR technologies that struggle with complex document layouts with structural variability, and helps transform unstructured documents into actionable and machine-usable representations. This has several downstream benefits such as increasing the availability of training-data for Large Language Models (LLMs), improving the accuracy of extractor, curator, retriever and AI agentic applications, and enhancing document understanding pipelines. NVIDIA-Nemotron-Parse v1.2 extends the prompt interface with a fourth prompt token category — **Text-in-picture prompts** — controlling whether the model extracts text from embedded images (e.g. photographs, figures): - ` `: extract text found inside pictures. - ` `: skip text extraction from pictures, reducing generation time by avoiding unnecessary tokens.

Key features

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Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Sources & traceability

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