NVIDIA Nemotron Parse 1.1
We introduce Nemotron-Parse-1.1, a lightweight document parsing and OCR model that advances the capabilities of its predecessor, Nemoretriever-Parse-1.0. Nemotron-Parse-1.1 delivers improved capabilities across general OCR, markdown formatting, structured table parsing, and text extraction from pictures, charts, and diagrams. It also supports a longer output sequence length for visually dense documents. As with its predecessor, it extracts bounding boxes of text segments, as well as corresponding semantic classes. Nemotron-Parse-1.1 follows an encoder-decoder architecture with 885M parameters, including a compact 256M-parameter language decoder. It achieves competitive accuracy on public benchmarks making it a strong lightweight OCR solution. We release the model weights publicly on Huggingface, as well as an optimized NIM container, along with a subset of the training data as part of the broader Nemotron-VLM-v2 dataset. Additionally, we release Nemotron-Parse-1.1-TC which operates on a reduced vision token length, offering a 20% speed improvement with minimal quality degradation.
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Kateryna Chumachenko
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Amala Sanjay Deshmukh
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2025-12-03T09:18:55.562Z
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Amala Sanjay Deshmukh
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user
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Jarno Seppanen
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Ilia Karmanov
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Chia-Chih Chen
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Lukas Voegtle
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Philipp Fischer
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Marek Wawrzos
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Saeid Motiian
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Roman Ageev
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Kedi Wu
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Alexandre Milesi
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Maryam Moosaei
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Krzysztof Pawelec
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Padmavathy Subramanian
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Mehrzad Samadi
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Xin Yu
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Celina Dear
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Sarah Stoddard
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Jenna Diamond
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Jesse Oliver
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Leanna Chraghchian
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Patrick Skelly
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Tom Balough
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Yao Xu
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Jane Polak Scowcroft
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Daniel Korzekwa
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Darragh Hanley
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Sandip Bhaskar
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Timo Roman
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Karan Sapra
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Andrew Tao
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Bryan Catanzaro
NVIDIA Nemotron Parse 1.1
We introduce Nemotron-Parse-1.1, a lightweight document parsing and OCR model that advances the capabilities of its predecessor, Nemoretriever-Parse-1.0. Nemotron-Parse-1.1 delivers improved capabilities across general OCR, markdown formatting, structured table parsing, and text extraction from pictures, charts, and diagrams. It also supports a longer output sequence length for visually dense documents. As with its predecessor, it extracts bounding boxes of text segments, as well as corresponding semantic classes. Nemotron-Parse-1.1 follows an encoder-decoder architecture with 885M parameters, including a compact 256M-parameter language decoder. It achieves competitive accuracy on public benchmarks making it a strong lightweight OCR solution. We release the model weights publicly on Huggingface, as well as an optimized NIM container, along with a subset of the training data as part of the broader Nemotron-VLM-v2 dataset. Additionally, we release Nemotron-Parse-1.1-TC which operates on a reduced vision token length, offering a 20% speed improvement with minimal quality degradation.
Nemotron-Parse-1.1 is a lightweight OCR and document parsing model with improved capabilities in general OCR, markdown formatting, structured table parsing, and text extraction from images, using an encoder-decoder architecture.
OCR document parsing markdown formatting structured table parsing text extraction encoder-decoder architecture bounding boxes semantic classes Huggingface NIM container vision tokens