NVIDIA Nemotron Parse 1.1
Authors: name: Kateryna Chumachenko; name: Amala Sanjay Deshmukh; name: Jarno Seppanen; name: Ilia Karmanov; name: Chia-Chih Chen; name: Lukas Voegtle; name: Philipp Fischer; name: Marek Wawrzos; name: Saeid Motiian; name: Roman Ageev; name: Kedi Wu; name: Alexandre Milesi; name: Maryam Moosaei; name: Krzysztof Pawelec; name: Padmavathy Subramanian; name: Mehrzad Samadi; name: Xin Yu; name: Celina Dear; name: Sarah Stoddard; name: Jenna Diamond; name: Jesse Oliver; name: Leanna Chraghchian; name: Patrick Skelly; name: Tom Balough; name: Yao Xu; name: Jane Polak Scowcroft; name: Daniel Korzekwa; name: Darragh Hanley; name: Sandip Bhaskar; name: Timo Roman; name: Karan Sapra; name: Andrew Tao; name: Bryan Catanzaro
Published: 2025-11-25 16:41:25

Abstract: 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.