# NeMo Retriever OCR v1 Overview ## 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 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, 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 **GOVERNING TERMS:** The trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). Use of this model 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/). ### 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 **Build.NVIDIA.com:** 2025-07 via [link](https://build.nvidia.com/nvidia/nemoretriever-ocr) ## 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. **Parameter Counts:** | Component | Parameters | |-------------------|-------------| | Detector | 45,268,472 | | Recognizer | 4,944,346 | | Relational model | 2,254,422 | | **Total** | 52,467,240 | ## Input **Input Type(s):** Image (RGB, PNG/JPEG, float32/uint8), aggregation level (word, sentence, or paragraph)
**Input Format(s):** List of strings
**Input Parameters:** 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 Related to Input:** Handles both single images and batches. Automatic multi-scale resizing for best accuracy.
## Output **Output Type(s)**: Structured OCR results: a list of detected text regions (bounding boxes), recognized text, and confidence scores
**Output Format(s)**: Bounding boxes: tuple of floats, recognized text: string, confidence score: float
** Output parameters**: Bounding boxes: 1D list of bounding box coordinates, recognized text: 1D list of strings, confidence score: 1D list of floats
**Other Properties Related to Output:** 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 (such as GPU cores) and software frameworks (such as CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
## Software Integration Runtime Engine: PyTorch ### Supported Hardware Microarchitecture Compatibility * NVIDIA Ampere * NVIDIA Blackwell * NVIDIA Hopper * NVIDIA Lovelace ### Preferred Operating System(s) Linux ### Model Version(s) NeMo Retriever OCR v1 ## Training, Testing, and Evaluation Datasets ### Training Dataset 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 by dataset:** Hybrid: Automated, Human, Synthetic * **Labeling Method by dataset:** Hybrid: Automated, Human, Synthetic * **Properties:** Includes scanned documents, natural scene images, receipts, and business documents. ### Testing Dataset * **Data Collection Method by dataset:** Hybrid: Automated, Human, Synthetic * **Labeling Method by dataset:** Hybrid: Automated, Human, Synthetic * **Properties:** Includes scanned documents, natural scene images, receipts, and business documents. ### Evaluation Dataset 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 by dataset:** Hybrid: Automated, Human, Synthetic * **Labeling Method by dataset:** Hybrid: Automated, Human, Synthetic * **Properties:** Benchmarks include challenging scene images, documents with varied layouts, and multi-language data. #### Benchmark 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 Acceleration Engine: PyTorch Test Hardware: * L40s ## 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. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).