README.md

---
license: other
license_name: nvidia-open-model-license
license_link: >-
  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
language:
- en
pipeline_tag: image-to-text
arxiv: None
tags:
- image
- ocr
- object recognition
- text recognition
- layout analysis
- ingestion
---

# NeMo Retriever OCR v1

## **Model Overview**

![viz.png](viz.png)

*Preview of the model output on the example image.*

### **Description**

The NeMo Retriever OCR v1 model is a state-of-the-art 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. NeMo Retriever OCR v1 has been developed to be production-ready and commercially usable, with a focus on speed and accuracy on both document and natural scene images.

The NeMo Retriever OCR v1 model is part of the NVIDIA NeMo Retriever collection of NIM microservices, 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 & development research assistants.

This model is ready for commercial use.

### **License/Terms of use**

The use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).

**You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.**

### Team

- Mike Ranzinger
- Bo Liu
- Theo Viel
- Charles Blackmon-Luca
- Oliver Holworthy
- Edward Kim
- Even Oldridge

Correspondence to Mike Ranzinger ([email protected]) and Bo Liu ([email protected])

### Deployment Geography

Global

### Use Case

The **NeMo Retriever OCR v1** model is designed for high-accuracy and high-speed extraction of textual information from images, 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 performance and efficiency make it an excellent choice for next-generation AI systems that demand both precision and scalability across diverse real-world content.

### Release Date

10/23/2025 via https://huggingface.co/nvidia/nemoretriever-ocr-v1

### References

- Technical blog: https://developer.nvidia.com/blog/approaches-to-pdf-data-extraction-for-information-retrieval/

### **Model Architecture**

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

The NeMo Retriever OCR v1 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, and production-ready OCR for diverse document and scene images.

**Network Architecture**: RegNetY-8GF

**Parameter Counts:**

| Component         | Parameters  |
|-------------------|-------------|
| Detector          | 45,268,472  |
| Recognizer        | 4,944,346   |
| Relational model  | 2,254,422   |
| **Total**         | 52,467,240  |

### **Input**

| Property         | Value              |
|------------------|-------------------|
| Input Type & Format       | Image (RGB, PNG/JPEG, float32/uint8), aggregation level (word, sentence, or paragraph) |
| Input Parameters (Two-Dimensional)      | 3 x H x W (single image) or B x 3 x H x W (batch) |
| Input Range      | [0, 1] (float32) or [0, 255] (uint8, auto-converted) |
| Other Properties | Handles both single images and batches. Automatic multi-scale resizing for best accuracy. |

### **Output**

| Property        | Value              |
|-----------------|-------------------|
| Output Type     | Structured OCR results: a list of detected text regions (bounding boxes), recognized text, and confidence scores |
| Output Format   | Bounding boxes: tuple of floats, recognized text: string, confidence score: float |
| Output Parameters | Bounding boxes: One-Dimenional (1D) list of bounding box coordinates, recognized text: One-Dimenional (1D) list of strings, confidence score: One-Dimenional (1D) list of floats |
| Other Properties | Please see the sample output for an example of the model 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

The model requires torch, and the custom code available in this repository.

1. Clone the repository

- Make sure git-lfs is installed (https://git-lfs.com)
```
git lfs install
```
- Using https
```
git clone https://huggingface.co/nvidia/nemoretriever-ocr-v1
```
- Or using ssh
```
git clone [email protected]:nvidia/nemoretriever-ocr-v1
```

2. Install the dependencies

- TODO

3. Run the model using the following code:

- TODO

<!---
### Software Integration

**Runtime Engine(s):**
- **NeMo Retriever Page Elements v3** NIM


**Supported Hardware Microarchitecture Compatibility [List in Alphabetic Order]:**
- NVIDIA Ampere
- NVIDIA Hopper
- NVIDIA Lovelace

**Preferred/Supported Operating System(s):**
- 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.
This AI model can be embedded as an Application Programming Interface (API) call into the software environment described above.
--->

## Model Version(s):

* `nemoretriever-ocr-v1`

## **Training and Evaluation Datasets:**

### **Training Dataset**

**Data Modality** 
* Image

**Image Training Data Size** 
* Less than a Million Images

The model is trained on a large-scale, curated mix of public and proprietary OCR datasets, focusing on high diversity of document layouts and scene images. The training set includes synthetic and real images with varied noise and backgrounds, filtered for commercial use eligibility.

**Data Collection Method:** Hybrid (Automated, Human, Synthetic)<br>
**Labeling Method:** Hybrid (Automated, Human, Synthetic)<br>
**Properties:** Includes scanned documents, natural scene images, receipts, and business documents.

### **Evaluation Datasets**

The NeMo Retriever OCR v1 model is evaluated on several NVIDIA internal datasets for various tasks, such as pure OCR, table content extraction, and document retrieval.

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

### **Evaluation Results**

We benchmarked NeMo Retriever OCR v1 on internal evaluation datasets against PaddleOCR on various tasks, such as pure OCR (Character Error Rate), table content extraction (TEDS), and document retrieval (Recall@5).

| Metric                                   | NeMo Retriever OCR v1 | PaddleOCR | Net change |
|-------------------------------------------|--------------------|-----------|-----------------|
| Character Error Rate                      | 0.1633             | 0.2029    | -19.5% ✔️         |
| Bag-of-character Error Rate               | 0.0453             | 0.0512    | -11.5% ✔️         |
| Bag-of-word Error Rate                    | 0.1203             | 0.2748    | -56.2% ✔️         |
| Table Extraction TEDS                     | 0.781              | 0.781     | 0.0% ⚖️           |
| Public Earnings Multimodal Recall@5       | 0.779              | 0.775     | +0.5% ✔️          |
| Digital Corpora Multimodal Recall@5       | 0.901              | 0.883     | +2.0% ✔️          |

### **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**<br>
**Acceleration Engine:** TensorRT, PyTorch<br>
**Test Hardware:** H100 PCIe/SXM, A100 PCIe/SXM, L40s, L4, and A10G<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 supporting model team to ensure this model meets requirements for the relevant industry and use case, and address unforeseen product misuse.

For more detailed information on ethical considerations for this model, see the Model Card++ tab for the Explainability, Bias, Safety & Security, and Privacy subcards.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).

## Bias

| Field | Response |
| ----- | ----- |
| Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing | None |
| Measures taken to mitigate against unwanted bias | None |


## Explainability

| Field | Response |
| ----- | ----- |
| Intended Task/Domain: | Optical Character Recognition (OCR) with a focus on retrieval application and documents. |
| Model Type: | Hybrid neural network with convolutional detector, transformer recognizer, and document structure modeling. |
| Intended Users: | Developers and teams building AI-driven search applications, retrieval-augmented generation (RAG) workflows, multimodal agents, or document intelligence applications. It is ideal for those working with large collections of scanned or photographed documents, including PDFs, forms, and reports. |
| Output: | Structured OCR results, including detected bounding boxes, recognized text, and confidence scores. |
| Describe how the model works: | The model first detects text regions in the image, then transcribes recognized text, and finally analyzes document structure and reading order. Outputs structured, machine-readable results suitable for downstream search and analysis. |
| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable |
| Technical Limitations: | This model version supports English only. |
| Verified to have met prescribed NVIDIA quality standards: | Yes |
| Performance Metrics: | Accuracy (e.g., character error rate), throughput, and latency. |
| Potential Known Risks: | The model may not always extract or transcribe all text with perfect accuracy, particularly in cases of poor image quality or highly stylized fonts. |
| Licensing & Terms of Use: | GOVERNING TERMS: The NIM container is governed by the [NVIDIA Software and Model Evaluation License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-and-model-evaluation-license/). Model License: [NVIDIA Software and Model Evaluation License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-and-model-evaluation-license/). |



## Privacy

| Field | Response |
| ----- | ----- |
| Generatable or reverse engineerable personal data? | No |
| Personal data used to create this model? | None Known |
| How often is dataset reviewed? | The dataset is initially reviewed when added, and subsequent reviews are conducted as needed or in response to change requests. |
| Is there provenance for all datasets used in training? | Yes |
| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |
| Is data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data. |
| Applicable Privacy Policy | https://www.nvidia.com/en-us/about-nvidia/privacy-policy/ |


## Safety

| Field | Response |
| ----- | ----- |
| Model Application Field(s): | Text recognition and structured OCR for multimodal retrieval. Inputs can include natural scene images, scanned documents, charts, tables, and infographics. |
| Use Case Restrictions: | Abide by [NVIDIA Software and Model Evaluation License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-and-model-evaluation-license/).   |
| Model and dataset restrictions: | The principle of least privilege (PoLP) is applied, limiting access for dataset generation and model development. Restrictions enforce dataset access only during training, and all dataset license constraints are adhered to. |
| Describe the life critical impact (if present): | Not applicable. |