nemotron-ocr-v2

Nemotron OCR v2

Model Overview

Description

Nemotron OCR v2 is a state-of-the-art multilingual text recognition model designed for robust end-to-end optical character recognition (OCR) on complex real-world images. It integrates three core neural network modules: a detector for text region localization, a recognizer for transcription of detected regions, and a relational model for layout and structure analysis.

This model is optimized for a wide variety of OCR tasks, including multi-line, multi-block, and natural scene text, and it supports advanced reading order analysis via its relational model component. Nemotron OCR v2 supports multiple languages and has been developed to be production-ready and commercially usable, with a focus on speed and accuracy on both document and natural scene images.

Nemotron OCR v2 is part of the NVIDIA NeMo Retriever collection, which provides state-of-the-art, commercially-ready models and microservices optimized for the lowest latency and highest throughput. It features a production-ready information retrieval pipeline with enterprise support. The models that form the core of this solution have been trained using responsibly selected, auditable data sources. With multiple pre-trained models available as starting points, developers can readily customize them for domain-specific use cases, such as information technology, human resource help assistants, and research and development assistants.

This model is ready for commercial use.

License/Terms of use

The model is governed by the NVIDIA Open Model License Agreement. Additional Information:Apache 2.0

Release Date: <br>

Hugging Face (this repo): nvidia/nemotron-ocr-v2 <br> Build.Nvidia.com 04/15/2026 via https://build.nvidia.com/nvidia/nemotron-ocr-v2 <br> NGC 04/15/2026 via https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo-microservices/containers/nemoretriever-ocr-v2 <br>

Deployment Geography

Global

Use Case

Nemotron OCR v2 is designed for high-accuracy and high-speed extraction of textual information from images across multiple languages, making it ideal for powering multimodal retrieval systems, Retrieval-Augmented Generation (RAG) pipelines, and agentic applications that require seamless integration of visual and language understanding. Its robust multilingual performance and efficiency make it an excellent choice for next-generation AI systems that demand both precision and scalability across diverse real-world content.

Model Architecture

Architecture Type: Hybrid detector-recognizer with document-level relational modeling

Nemotron OCR v2 is available in two variants:

• v2_english — Optimized for English-language OCR with word-level region handling.
• v2_multilingual — Supports English, Chinese (Simplified and Traditional), Japanese, Korean, and Russian with line-level region handling for multilingual documents.

Both variants share the same three-component architecture:

• Text Detector: Utilizes a RegNetX-8GF convolutional backbone for high-accuracy localization of text regions within images.
• Text Recognizer: Employs a pre-norm Transformer-based sequence recognizer to transcribe text from detected regions, supporting variable word and line lengths.
• Relational Model: Applies a multi-layer global relational module to predict logical groupings, reading order, and layout relationships across detected text elements.

All components are trained jointly in an end-to-end fashion, providing robust, scalable, and production-ready OCR for diverse document and scene images.

Network Architecture: RegNetX-8GF

Recognizer Comparison

The two variants share an identical detector and relational architecture but differ in recognizer capacity:

Parameter Counts

v2_english (from v2_english/):

v2_multilingual (from v2_multilingual/):

Input

Output

Sample output

ocr_boxes = [[[15.552736282348633, 43.141815185546875],
  [150.00149536132812, 43.141815185546875],
  [150.00149536132812, 56.845645904541016],
  [15.552736282348633, 56.845645904541016]],
 [[298.3145751953125, 44.43315124511719],
  [356.93585205078125, 44.43315124511719],
  [356.93585205078125, 57.34814453125],
  [298.3145751953125, 57.34814453125]],
 [[15.44686508178711, 13.67985725402832],
  [233.15859985351562, 13.67985725402832],
  [233.15859985351562, 27.376562118530273],
  [15.44686508178711, 27.376562118530273]],
 [[298.51727294921875, 14.268900871276855],
  [356.9850769042969, 14.268900871276855],
  [356.9850769042969, 27.790447235107422],
  [298.51727294921875, 27.790447235107422]]]

ocr_txts = ['The previous notice was dated',
 '22 April 2016',
 'The previous notice was given to the company on',
 '22 April 2016']

ocr_confs = [0.97730815, 0.98834222, 0.96804602, 0.98499225]

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Usage

Prerequisites

• OS: Linux amd64 with NVIDIA GPU
• CUDA toolkit with nvcc on PATH. The toolkit version must be compatible with the version of PyTorch you install (same major version). For example, if you install torch with CUDA 12.8 bindings, you need CUDA toolkit 12.x. Verify with nvcc --version and nvidia-smi.
• Python: 3.12 (the package requires >=3.12,<3.13)
• Build tools (for the C++ CUDA extension compiled at install time):
  • GCC/G++ with C++17 support
  • CUDA toolkit headers
  • OpenMP

Installation

The package includes a C++ CUDA extension that is compiled during installation. Because the extension must be built against the same PyTorch CUDA version as your system's CUDA toolkit, install PyTorch first, then install this package with --no-build-isolation so it uses your existing PyTorch.

1. Clone the repository

• Make sure git-lfs is installed (https://git-lfs.com)

git lfs install
git clone https://huggingface.co/nvidia/nemotron-ocr-v2

1. Installation

With pip

• Create and activate a Python 3.12 environment
• Install PyTorch matching your CUDA toolkit (see https://pytorch.org/get-started/locally/):

# Example for CUDA 12.8:
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu128

• Install the package:

cd nemotron-ocr
pip install --no-build-isolation -v .

• Verify the C++ extension loads:

python -c "from nemotron_ocr.inference.pipeline_v2 import NemotronOCRV2; print('OK')"

With docker

Run the example end-to-end without installing anything on the host (besides Docker, docker compose, and NVIDIA Container Toolkit):

• Ensure Docker can see your GPU:

docker run --rm --gpus all nvcr.io/nvidia/pytorch:25.09-py3 nvidia-smi

• From the repo root, bring up the service to run the example (sample image ocr-example-input-1.png when present):

docker compose run --rm nemotron-ocr \
  bash -lc "python example.py ocr-example-input-1.png --merge-level paragraph"

This will:

• Build an image from the provided Dockerfile (based on nvcr.io/nvidia/pytorch)
• Mount the repo at /workspace
• Run example.py (downloads v2 multilingual from Hugging Face on first run unless you pass --model-dir)

Output is saved next to your input image as <name>-annotated.<ext> on the host.

1. Run the model using the following code.

Use nemotron_ocr.inference.pipeline_v2.NemotronOCRV2. With no arguments, checkpoints are downloaded from Hugging Face: by default the v2 multilingual bundle (nvidia/nemotron-ocr-v2 / v2_multilingual/). Use lang="en" for the English v2 build (nvidia/nemotron-ocr-v2 / v2_english/), or pass model_dir to load from disk (any complete checkpoint folder; lang is then ignored).

from nemotron_ocr.inference.pipeline_v2 import NemotronOCRV2

# Default: Hugging Face v2 multilingual
ocr = NemotronOCRV2()

# English v2 (Hub, word-level)
ocr_en = NemotronOCRV2(lang="en")

# Multilingual v2 explicitly (same default as NemotronOCRV2())
# Uses the line-level variant.
ocr_multi = NemotronOCRV2(lang="multi")

# Local directory with detector.pth, recognizer.pth, relational.pth, charset.txt
ocr_local = NemotronOCRV2(model_dir="./v2_multilingual")

predictions = ocr("ocr-example-input-1.png")

for pred in predictions:
    print(
        f"  - Text: '{pred['text']}', "
        f"Confidence: {pred['confidence']:.2f}, "
        f"Bbox: [left={pred['left']:.4f}, upper={pred['upper']:.4f}, right={pred['right']:.4f}, lower={pred['lower']:.4f}]"
    )

Inference modes

# Detector only — returns bounding boxes without text recognition.
# Loads only the detector (~37% less GPU memory, ~20% faster).
ocr_det = NemotronOCRV2(detector_only=True)
boxes = ocr_det("page.png")
# Each prediction has: confidence, left, right, upper, lower, quad

# Skip relational — returns per-word text without reading-order grouping.
# Skips the relational model (~35% less GPU memory, ~8% faster).
ocr_fast = NemotronOCRV2(skip_relational=True)
words = ocr_fast("page.png", merge_level="word")
# Each prediction has: text, confidence, left, right, upper, lower

# Profiling mode — enables per-phase CUDA-synced timing in the logs.
import logging
logging.basicConfig(level=logging.INFO)
ocr_profile = NemotronOCRV2(verbose_post=True)

Constructor rules

• You can choose model weights with either lang or model_dir.
• lang (keyword only, Hub download path):
  • None, "multi", or "multilingual" -> v2 multilingual (default): nvidia/nemotron-ocr-v2 / v2_multilingual/
  • "en" or "english" -> v2 English: nvidia/nemotron-ocr-v2 / v2_english/
  • "v1" or "legacy" -> v1 English-only (backward compatibility): fetched from nvidia/nemotron-ocr-v1 if not already cached locally
  • Both v2 variants (v2_multilingual/ and v2_english/) are included in this repository.
• model_dir (local override): if it points to a complete local checkpoint directory (detector.pth, recognizer.pth, relational.pth, charset.txt), it takes precedence and overrides lang.
• If model_dir is provided but incomplete, loading falls back to Hub resolution via lang (defaulting to v2 multilingual when lang is None).

Software Integration

Runtime Engine(s):

• PyTorch

Supported Hardware Microarchitecture Compatibility:

• NVIDIA Ampere
• NVIDIA Blackwell
• NVIDIA Hopper
• NVIDIA Lovelace

Preferred/Supported Operating System(s):

• Linux

Model Version(s)

• This repository: Nemotron OCR v2 with both variants: v2_english/ and v2_multilingual/.
• Hugging Face Hub: nvidia/nemotron-ocr-v2.

Training and Evaluation Datasets:

Training Dataset

Data Modality

• Image

Image Training Data Size

• Approximately 12 million images

The model is trained on a large-scale, curated mix of real-world and synthetic OCR datasets spanning multiple languages, scripts, and document types.

Real-world datasets (~680K images): Natural scene text, multilingual scene text, arbitrary-shaped text, chart and infographic text, table images with bilingual annotations, and handwritten document pages. These cover diverse layouts, languages, and document types.

Synthetic datasets (~11M+ images): Rendered multilingual document pages in six languages (English, Japanese, Korean, Russian, Chinese Simplified, and Chinese Traditional) and synthetic historical document crops covering archaic characters with degradation effects.

Data Collection Method by dataset: Hybrid (Automated, Human, Synthetic)<br> Labeling Method by dataset: Hybrid (Automated, Human, Synthetic)<br> Properties: Includes scanned documents, natural scene images, charts, tables, infographics, handwritten documents, and synthetic rendered pages in multiple languages and scripts.

Evaluation Datasets

Nemotron OCR v2 is evaluated on OmniDocBench, a comprehensive document OCR benchmark covering English, Chinese, and mixed-language content across diverse document categories.

Data Collection Method by dataset: Hybrid (Automated, Human, Synthetic)<br> Labeling Method by dataset: Hybrid (Automated, Human, Synthetic)<br> Properties: Benchmarks include challenging scene images, documents with varied layouts, and multi-language data.

Evaluation Results

Tables below are reference metrics from NVIDIA’s benchmark runs (OmniDocBench, SynthDoG). Reproducing them requires datasets and scripts that are not checked into this Hugging Face repository.

OmniDocBench

Normalized Edit Distance (NED) sample_avg on OmniDocBench (lower = better). Results follow OmniDocBench methodology (empty predictions skipped). All models evaluated in crop mode. Speed measured on a single A100 GPU.

Column key: pages/s is throughput using the v2 batched pipeline where measured; EN = English, ZH = Simplified Chinese, Mixed = English/Chinese mixed, White/Single/Multi = background type, Normal/Rotate90/Rotate270/Horizontal = text orientation.

SynthDoG Generated Benchmark Data

Normalized Edit Distance (NED) page_avg on SynthDoG generated benchmark data (lower = better):

Detailed Performance Analysis

The model demonstrates robust multilingual performance on complex layouts, noisy backgrounds, and challenging real-world scenes. Reading order and block detection are powered by the relational module, supporting downstream applications such as chart-to-text, table-to-text, and infographic-to-text extraction.

Inference<br> Acceleration Engine: PyTorch<br> Supported Hardware: H100 PCIe/SXM, A100 PCIe/SXM, L40S, L4, A10G, H200 NVL, B200, RTX PRO 6000 Blackwell Server Edition<br>

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. <br> The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment. <br> Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included. <br> For more detailed information on ethical considerations for this model, please see the Explainability, Bias, Safety & Security, and Privacy sections below. <br> Please report security vulnerabilities or NVIDIA AI Concerns here.

Bias

Explainability

Privacy

Safety