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
Organization: NVIDIA

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.