nemotron-ocr-v1

Description

nemotron-ocr-v1 is a text recognition model designed for robust end-to-end optical character recognition (OCR) on complex real-world images and documents. 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 reading-order analysis.

This model is optimized for a wide variety of OCR tasks, including multi-line, multi-block, and natural scene text, and supports advanced reading order analysis via its relational model component. It is production-ready with a focus on speed and accuracy on both document and natural scene images.

This model is ready for commercial/non-commercial use.

License and Terms of Use:

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Model Developer: NVIDIA

Deployment Geography:

Global

Use Case:

This model is designed for high-accuracy and high-speed OCR to support multimodal retrieval systems, RAG pipelines, and document intelligence applications that require extraction of text and structure from images and scanned documents.

Release Date:

Build.NVIDIA.com 03/02/2026 via nemotron-ocr-v1

Reference(s):

References:

• NVIDIA NIM Documentation

Model Architecture:

Architecture Type: Hybrid detector–recognizer with document-level relational modeling
The model integrates three specialized neural components:

• Text Detector: Utilizes a RegNetY-8GF convolutional backbone for high-accuracy localization of text regions within images.
• Text Recognizer: Employs a 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 OCR for diverse document and scene images.

Parameter Counts:

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.

Software Integration:

Runtime Engines: TensorRT
Supported Hardware Microarchitecture Compatibility:
NVIDIA Ampere
NVIDIA Blackwell
NVIDIA Hopper
NVIDIA Lovelace
Operating Systems: Linux

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.

Model Version(s)

nemotron-ocr-v1

Short Name: nemotron-ocr-v1

Training and Evaluation Datasets:

Training Dataset

Data Modality: Image
Training Data Collection: Hybrid (Automated, Human, Synthetic)
Training Labeling: Hybrid (Automated, Human, Synthetic)
Training Properties: Trained on a large-scale, curated mix of public and proprietary OCR datasets, focusing on high diversity of document layouts and natural scene images. The training set includes synthetic and real images with varied noise and backgrounds, filtered for commercial use eligibility. Includes scanned documents, natural scene images, receipts, and business documents.

Evaluation Dataset

Evaluation Data Collection: Hybrid (Automated, Human, Synthetic)
Evaluation Labeling: Hybrid (Automated, Human, Synthetic)
Evaluation Properties: Evaluated on several NVIDIA internal datasets for pure OCR, table content extraction, and document retrieval. Benchmarks include challenging scene images, documents with varied layouts, and multi-language data.

Evaluation Results

Benchmarked against PaddleOCR on internal evaluation datasets across OCR (Character Error Rate), table extraction (TEDS), and document retrieval (Recall@5).

Detailed Performance Analysis

The model demonstrates robust 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

Acceleration Engine: TensorRT, PyTorch
Test Hardware: H100 PCIe/SXM, A100 PCIe/SXM, L40s, L4, A10G

Ethical Considerations

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