NVIDIA Nemotron Parse 1.1

TL;DR

Nemotron-Parse-1.1 is a lightweight, advanced OCR and document parsing model that improves upon previous versions with better accuracy, longer output capabilities, and faster processing, suitable for diverse document types.

Contribution

It introduces a new lightweight encoder-decoder OCR model with enhanced features, longer output sequences, and publicly released weights and datasets.

Findings

Achieves competitive accuracy on public benchmarks.

Supports longer output sequences for dense documents.

Offers a faster variant with minimal quality loss.

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.