nvCLIP4STR Model Card

nvCLIP4STR Overview

nvCLIP4STR is optical character recognition network, which aims to recognize characters from the images. One pretrained nvCLIP4STR model is delivered, which is trained on 7 dataset with alphanumeric labels. This model is ready for commercial use.

Deployment Geography:

Global

Use Case

This model can be used to recognize texts in an image in computer vision applications.

Release Date:

NGC [05/30/2025]

License

License to use these models is covered by the NVIDIA Open Model License. By downloading the model, you accept the terms and conditions of these licenses.

Model Architecture

Architecture Type: Convolution Neural Network + Transformer Encoder Decoder.\nNetwork Architecture:\nThis model was developed based on the CLIP4STR, which is a optical character recognition model. The nvCLIP4STR has two encoder decoder branches: a visual branch and a cross-modal branch. The visual branch provides an initial prediction based on the visual feature, and the cross-modal branch refines this prediction by addressing the discrepancy between the visual feature and text semantics.

Input

Input Type: Image
Input Formats: Red, Green, Blue (RGB)
Input Parameters: Two-Dimensional (2D)
Other Properties Related to Input: 224x224 resolution required; no alpha channel or bits

Output

Output Type(s): Text
Output Format: String
Output Parameters: One-Dimensional (1D)
Other Properties Related to Output: Texts including numbers, capital and lower-case letters and symbols.

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 Engine(s):

TAO 6.0.0

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Jetson
NVIDIA Hopper
NVIDIA Volta

[Preferred/Supported] Operating System(s):

Linux

Model versions:

deployable_v1.0 - Models deployable with CLIP-based backbone.

Training and Evaluation Datasets

The total size: 6M images
Total number of datasets: 7 training datasets, 3 evaluation datasets
Dataset partition: training and evaluation are different datasets

Training Datasets

Link:

Data Collection Method by dataset:

Hybrid: Automated, Human

Labeling Method by dataset:

Hybrid: Automated, Human

Properties:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

dataset	image numbers
ICDAR2015	4468
MLT19	45551
Uber-Text	91978
COCO-Text v2.0	59820
OpenVINO	1912794
TextOCR	714770
Union14M-L	3220666

Evaluation Datasets

Link:

Data Collection Method by dataset:

Hybrid: Automated, Human

Labeling Method by dataset:

Hybrid: Automated, Human

Properties:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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dataset	eval image numbers
ICDAR13	1015
ICDAR15	2077
Uber-Text	80551

Performance

We test the nvCLIP4STR model on three different datasets: Uber-Text validation dataset, ICDAR13 and ICDAR15 scene text benchmark dataset. And we compare nvCLIP4STR against the previous OCRNet in TAO.

Methodology and KPI

The key performance indicator is the accuracy of character recognition. The accurate recognition means all the characters in a text area are recognized correctly. The KPIs for the evaluation data are reported below.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

model	dataset	accuracy
nvCLIP4STR	Uber-Text	89.34%
nvCLIP4STR	ICDAR13	98.42%
nvCLIP4STR	ICDAR15	90.08%
ocrnet_resnet50_unpruned	Uber-Text + TextOCR validation	77.1%
ocrnet_resnet50_unpruned	ICDAR13	91.8%
ocrnet_resnet50_unpruned	ICDAR15	78.6%
ocrnet_resnet50_unpruned	Internal PCB validation	74.1%
ocrnet_resnet50_pruned	ICDAR13	92.6%
ocrnet-vit	Uber-Text + TextOCR validation	83.7%
ocrnet-vit	ICDAR13	95.5%
ocrnet-vit	ICDAR15	84.7%
ocrnet-vit-pcb	Internal PCB validation	84.2%

Inference

Acceleration Engine: TensorRT

Test Hardware:

DGX A100

How to use this model

This model needs to be used with NVIDIA Hardware and Software. For Hardware, the model can run on any NVIDIA GPU including NVIDIA Jetson devices. This model can only be used with NVIDIA-Optical-Character-Detection-and-Recognition-Solution (nvOCDR lib), you can try to deploy nvCLIP4STR and run c++ inference in this nvOCDR lib

Primary use case intended for this model is to recognize the characters from the detected text region.

There is one type of nvCLIP4STR provided:

deployable (unpruned)

The deployable model is in onnx format. The deployable models can be deployed in TensorRT and nvOCDR.

Instructions to use the model with nvOCDR

nvOCDR lib can support nvCLIP4STR now. For more information of c++ inference, please refer to the nvCLIP4STR C++ Sample.

Using TAO Pre-trained Models

Get TAO Container
Get other purpose-built models from the NGC model registry:\n
- TrafficCamNet
- PeopleNet
- PeopleNet
- PeopleNet-Transformer
- DashCamNet
- FaceDetectIR
- VehicleMakeNet
- VehicleTypeNet
- PeopleSegNet
- PeopleSemSegNet
- License Plate Detection
- License Plate Recognition
- Gaze Estimation
- Facial Landmark
- Heart Rate Estimation
- Gesture Recognition
- Emotion Recognition
- FaceDetect
- 2D Body Pose Estimation
- ActionRecognitionNet
- ActionRecognitionNet
- PoseClassificationNet
- People ReIdentification
- PointPillarNet
- CitySegFormer
- Retail Object Detection
- Retail Object Embedding
- Optical Inspection
- Optical Character Detection
- Optical Character Recognition
- PCB Classification
- PeopleSemSegFormer
- LPDNet
- License Plate Recognition
- Gaze Estimation
- Facial Landmark
- Heart Rate Estimation
- Gesture Recognition
- Emotion Recognition
- FaceDetect
- 2D Body Pose Estimation
- ActionRecognitionNet
- ActionRecognitionNet
- PoseClassificationNet
- People ReIdentification
- PointPillarNet
- CitySegFormer
- Retail Object Detection
- Retail Object Embedding
- Optical Inspection
- Optical Character Detection
- Optical Character Recognition
- PCB Classification
- PeopleSemSegFormer
\n

Technical blogs

Access the latest in Vision AI development workflows with NVIDIA TAO Toolkit 5.0
Improve accuracy and robustness of vision ai models with vision transformers and NVIDIA TAO
Train like a ‘pro’ without being an AI expert using TAO AutoML
Create Custom AI models using NVIDIA TAO Toolkit with Azure Machine Learning
Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO
Learn endless ways to adapt and supercharge your AI workflows with TAO - Whitepaper
Customize Action Recognition with TAO and deploy with DeepStream
Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - Part 1 | Part 2
Learn how to train real-time License plate detection and recognition app with TAO and DeepStream.
Model accuracy is extremely important, learn how you can achieve state of the art accuracy for classification and object detection models using TAO

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. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns here.

Reference

Zhao, Shuai, et al. \"CLIP4STR: a simple baseline for scene text recognition with pre-trained vision-language model.\" IEEE Transactions on Image Processing (2024).
Radford, Alec, et al. \"Learning transferable visual models from natural language supervision.\" International conference on machine learning. PmLR, 2021.

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One pretrained nvCLIP4STR model is delivered, which is trained on 7 dataset with alphanumeric labels. This model is ready for commercial use.","# nvCLIP4STR Model Card\n\n## nvCLIP4STR Overview\n\nnvCLIP4STR is optical character recognition network, which aims to recognize characters from the images. One pretrained nvCLIP4STR model is delivered, which is trained on 7 dataset with alphanumeric labels. This model is ready for commercial use.\n\n## Deployment Geography:\nGlobal\n\n## Use Case\n\nThis model can be used to recognize texts in an image in computer vision applications.\n\n## Release Date: \n\nNGC [05/30/2025]\n\n## License\n\nLicense to use these models is covered by the NVIDIA Open Model License. By downloading the model, you accept the terms and conditions of these [licenses](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\n## Model Architecture\n\n**Architecture Type:** Convolution Neural Network + Transformer Encoder Decoder. \n**Network Architecture:**\nThis model was developed based on the [CLIP4STR](https://arxiv.org/abs/2305.14014), which is a optical character recognition model. The nvCLIP4STR has two encoder decoder branches: a visual branch and a cross-modal branch. The visual branch provides an initial prediction based on the visual feature, and the cross-modal branch refines this prediction by addressing the discrepancy between the visual feature and text semantics.\n\n### Input\n- **Input Type:** Image\n- **Input Formats:** Red, Green, Blue (RGB)\n- **Input Parameters:** Two-Dimensional (2D)\n- **Other Properties Related to Input:** 224x224 resolution required; no alpha channel or bits\n\n### Output\n- **Output Type(s):** Text\n- **Output Format:** String\n- **Output Parameters:** One-Dimensional (1D)\n- **Other Properties Related to Output:** Texts including numbers, capital and lower-case letters and symbols.\n\nOur 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.\n\n## Software Integration:\n### Runtime Engine(s):\n- TAO 6.0.0\n\n### Supported Hardware Microarchitecture Compatibility:\n- NVIDIA Ampere \n- NVIDIA Jetson \n- NVIDIA Hopper \n- NVIDIA Volta\n\n### [Preferred/Supported] Operating System(s):\n- Linux\n\n## Model versions:\n- **deployable_v1.0** - Models deployable with CLIP-based backbone.\n\n## Training and Evaluation Datasets\n\n- **The total size:** 6M images\n- **Total number of datasets:** 7 training datasets, 3 evaluation datasets\n- **Dataset partition:** training and evaluation are different datasets\n\n### Training Datasets\n**Link:** \n* [ICDAR15](https://rrc.cvc.uab.es/?ch=4)\n* [MLT19](https://rrc.cvc.uab.es/?ch=15)\n* [Uber-Text](https://s3-us-west-2.amazonaws.com/uber-common-public/ubertext/index.html)\n* [COCO-Text v2.0](https://bgshih.github.io/cocotext/)\n* [OpenVINO](https://proceedings.mlr.press/v157/krylov21a.html)\n* [TextOCR](https://textvqa.org/textocr/)\n* [Union14M-L](https://github.com/Mountchicken/Union14M/tree/main?tab=readme-ov-file#31-union14m-l)\n\n**Data Collection Method by dataset:**
\n* Hybrid: Automated, Human
\n\n**Labeling Method by dataset:**
\n* Hybrid: Automated, Human
\n\n**Properties:**
\n |dataset|image numbers|\n |:---:|:---:|\n |ICDAR2015|4468|\n |MLT19|45551|\n |Uber-Text|91978|\n |COCO-Text v2.0|59820|\n |OpenVINO|1912794|\n |TextOCR|714770|\n |Union14M-L|3220666|\n\n### Evaluation Datasets\n\n**Link:** \n* [ICDAR13](https://rrc.cvc.uab.es/?ch=2)\n* [ICDAR15](https://rrc.cvc.uab.es/?ch=4)\n* [Uber-Text](https://s3-us-west-2.amazonaws.com/uber-common-public/ubertext/index.html)\n\n**Data Collection Method by dataset:**
\n* Hybrid: Automated, Human
\n\n**Labeling Method by dataset:**
\n* Hybrid: Automated, Human
\n\n**Properties:**
\n\n |dataset|eval image numbers|\n |:---:|:---:|\n |ICDAR13|1015|\n |ICDAR15|2077|\n |Uber-Text|80551|\n\n## Performance\n\nWe test the nvCLIP4STR model on three different datasets: Uber-Text validation dataset, ICDAR13 and ICDAR15 scene text benchmark dataset. And we compare nvCLIP4STR against the previous [OCRNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocrnet) in TAO.\n\n### Methodology and KPI\n\nThe key performance indicator is the accuracy of character recognition. The accurate recognition means all the characters in a text area are recognized correctly. The KPIs for the evaluation data are reported below. \n\n|model|dataset|accuracy|\n|---|---|---|\n|nvCLIP4STR|Uber-Text|89.34%|\n|nvCLIP4STR|ICDAR13|98.42%|\n|nvCLIP4STR|ICDAR15|90.08%|\n|ocrnet_resnet50_unpruned|Uber-Text + TextOCR validation|77.1%|\n|ocrnet_resnet50_unpruned|ICDAR13|91.8%|\n|ocrnet_resnet50_unpruned|ICDAR15|78.6%|\n|ocrnet_resnet50_unpruned|Internal PCB validation|74.1%|\n|ocrnet_resnet50_pruned|ICDAR13|92.6%|\n|ocrnet-vit|Uber-Text + TextOCR validation|83.7%|\n|ocrnet-vit|ICDAR13|95.5%|\n|ocrnet-vit|ICDAR15|84.7%|\n|ocrnet-vit-pcb|Internal PCB validation|84.2%|\n\n## Inference\n\n**Acceleration Engine:** TensorRT
\n\n**Test Hardware:**
\n- DGX A100\n\n\n## How to use this model\n\nThis model needs to be used with NVIDIA Hardware and Software. For Hardware, the model can run on any NVIDIA GPU including NVIDIA Jetson devices. This model can only be used with [NVIDIA-Optical-Character-Detection-and-Recognition-Solution](https://github.com/NVIDIA-AI-IOT/NVIDIA-Optical-Character-Detection-and-Recognition-Solution) (nvOCDR lib), you can try to deploy nvCLIP4STR and run c++ inference in this nvOCDR lib\n\nPrimary use case intended for this model is to recognize the characters from the detected text region. \n\nThere is one type of nvCLIP4STR provided:\n- deployable (unpruned)\n\nThe `deployable` model is in `onnx` format. The `deployable` models can be deployed in TensorRT and nvOCDR.\n\n### Instructions to use the model with nvOCDR\n\nnvOCDR lib can support nvCLIP4STR now. For more information of c++ inference, please refer to the [nvCLIP4STR C++ Sample](https://github.com/NVIDIA-AI-IOT/NVIDIA-Optical-Character-Detection-and-Recognition-Solution/tree/main/c%2B%2B_samples#nvclip4str).\n\n## Using TAO Pre-trained Models\n\n- Get [TAO Container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit)\n- Get other purpose-built models from the NGC model registry:\n - [TrafficCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:trafficcamnet)\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\n - [PeopleNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet)\n - [PeopleNet-Transformer](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplenet_transformer)\n - [DashCamNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:dashcamnet)\n - [FaceDetectIR](https://ngc.nvidia.com/catalog/models/nvidia:tao:facedetectir)\n - [VehicleMakeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehiclemakenet)\n - [VehicleTypeNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:vehicletypenet)\n - [PeopleSegNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplesegnet)\n - [PeopleSemSegNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:peoplesemsegnet)\n - [License Plate Detection](https://ngc.nvidia.com/catalog/models/nvidia:tao:lpdnet)\n - [License Plate Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:lprnet)\n - [Gaze Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:gazenet)\n - [Facial Landmark](https://ngc.nvidia.com/catalog/models/nvidia:tao:fpenet)\n - [Heart Rate Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:heartratenet)\n - [Gesture Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:gesturenet)\n - [Emotion Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:emotionnet)\n - [FaceDetect](https://ngc.nvidia.com/catalog/models/nvidia:tao:facenet)\n - [2D Body Pose Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:bodyposenet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [PoseClassificationNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:poseclassificationnet)\n - [People ReIdentification](https://ngc.nvidia.com/catalog/models/nvidia:tao:reidentificationnet)\n - [PointPillarNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pointpillarnet)\n - [CitySegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/citysemsegformer)\n - [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection)\n - [Retail Object Embedding](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_recognition)\n - [Optical Inspection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/optical_inspection)\n - [Optical Character Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocdnet)\n - [Optical Character Recognition](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocrnet)\n - [PCB Classification](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pcb_classification)\n - [PeopleSemSegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesemsegformer)\n - [LPDNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:lpdnet)\n - [License Plate Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:lprnet)\n - [Gaze Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:gazenet)\n - [Facial Landmark](https://ngc.nvidia.com/catalog/models/nvidia:tao:fpenet)\n - [Heart Rate Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:heartratenet)\n - [Gesture Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:gesturenet)\n - [Emotion Recognition](https://ngc.nvidia.com/catalog/models/nvidia:tao:emotionnet)\n - [FaceDetect](https://ngc.nvidia.com/catalog/models/nvidia:tao:facenet)\n - [2D Body Pose Estimation](https://ngc.nvidia.com/catalog/models/nvidia:tao:bodyposenet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [ActionRecognitionNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:actionrecognitionnet)\n - [PoseClassificationNet](https://ngc.nvidia.com/catalog/models/nvidia:tao:poseclassificationnet)\n - [People ReIdentification](https://ngc.nvidia.com/catalog/models/nvidia:tao:reidentificationnet)\n - [PointPillarNet](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pointpillarnet)\n - [CitySegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/citysemsegformer)\n - [Retail Object Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_detection)\n - [Retail Object Embedding](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/retail_object_recognition)\n - [Optical Inspection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/optical_inspection)\n - [Optical Character Detection](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocdnet)\n - [Optical Character Recognition](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/ocrnet)\n - [PCB Classification](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/pcb_classification)\n - [PeopleSemSegFormer](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tao/models/peoplesemsegformer)\n\n## Technical blogs\n\n- [Access the latest in Vision AI development workflows with NVIDIA TAO Toolkit 5.0](https://developer.nvidia.com/blog/access-the-latest-in-vision-ai-model-development-workflows-with-nvidia-tao-toolkit-5-0/)\n- [Improve accuracy and robustness of vision ai models with vision transformers and NVIDIA TAO](https://developer.nvidia.com/blog/improve-accuracy-and-robustness-of-vision-ai-apps-with-vision-transformers-and-nvidia-tao/)\n- [Train like a ‘pro’ without being an AI expert using TAO AutoML](https://developer.nvidia.com/blog/training-like-an-ai-pro-using-tao-automl/)\n- [Create Custom AI models using NVIDIA TAO Toolkit with Azure Machine Learning ](https://developer.nvidia.com/blog/creating-custom-ai-models-using-nvidia-tao-toolkit-with-azure-machine-learning/)\n- [Developing and Deploying AI-powered Robots with NVIDIA Isaac Sim and NVIDIA TAO](https://developer.nvidia.com/blog/developing-and-deploying-ai-powered-robots-with-nvidia-isaac-sim-and-nvidia-tao/)\n- Learn endless ways to adapt and supercharge your AI workflows with TAO - [Whitepaper](https://developer.nvidia.com/tao-toolkit-usecases-whitepaper/1-introduction)\n- [Customize Action Recognition with TAO and deploy with DeepStream](https://developer.nvidia.com/blog/developing-and-deploying-your-custom-action-recognition-application-without-any-ai-expertise-using-tao-and-deepstream/)\n- Read the 2 part blog on training and optimizing 2D body pose estimation model with TAO - [Part 1](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-1) | [Part 2](https://developer.nvidia.com/blog/training-optimizing-2d-pose-estimation-model-with-tao-toolkit-part-2)\n- Learn how to train [real-time License plate detection and recognition app](https://developer.nvidia.com/blog/creating-a-real-time-license-plate-detection-and-recognition-app) with TAO and DeepStream.\n- Model accuracy is extremely important, learn how you can achieve [state of the art accuracy for classification and object detection models](https://developer.nvidia.com/blog/preparing-state-of-the-art-models-for-classification-and-object-detection-with-tao-toolkit/) using TAO\n\n## Suggested reading\n\n- More information on about TAO Toolkit and pre-trained models can be found at the [NVIDIA Developer Zone](https://developer.nvidia.com/tao-toolkit)\n- Read the [TAO getting Started](https://docs.nvidia.com/tao/tao-toolkit/) guide and [release notes](https://docs.nvidia.com/tao/tao-toolkit/text/release_notes.html). \n- If you have any questions or feedback, please refer to the discussions on [TAO Toolkit Developer Forums](https://forums.developer.nvidia.com/c/accelerated-computing/intelligent-video-analytics/tao-toolkit/17)\n- Deploy your model on the edge using DeepStream. Learn more about DeepStream SDK https://developer.nvidia.com/deepstream-sdk\n\n## Ethical Considerations\n\nNVIDIA 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. For more detailed information on ethical considerations for this model, please see the Model Card++ [Explainability](./explainability-example.md), [Bias](./bias-example.md), [Safety & Security](./safety-example.md), and [Privacy](./privacy-example.md) Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n## Reference \n\n- Zhao, Shuai, et al. \"CLIP4STR: a simple baseline for scene text recognition with pre-trained vision-language model.\" IEEE Transactions on Image Processing (2024).\n- Radford, Alec, et al. \"Learning transferable visual models from natural language supervision.\" International conference on machine learning. PmLR, 2021.","displayName","nvCLIP4STR",["D",1754419705845],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/tao/models/nvclip4str","rawImageUrl","","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","| Field | Response |\n| -- | -- |\n| Participation considerations from adversely impacted groups [(protected classes)](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None of the above |\n| Measures taken to mitigate against unwanted bias: | None of the above.|","explainability","| Field | Response |\n| -- | -- |\n| Intended Application(s) & Domain(s): | Recognizing Text in Images |\n| Model Type: | Convolutional Neural Network + Transformer Encoder Decoder |\n| Intended Users: | The model is intended for developers that build computer vision applications to recognize texts in images. |\n| Output: | Text |\n| Describe how the model works: | Extracts features from provided images for text. |\n| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable |\n| Technical Limitations: | The model may struggle to identify handwritten texts in images and texts that are more than 80% occluded. |\n| Verified to have met prescribed NVIDIA standards: | Yes |\n| Performance Metrics: | Accuracy |\n| Potential Known Risks: | The model may not recognize texts that are more than 80% occluded. |\n| Licensing: | [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) |","privacy","| Field | Response |\n| -- | -- |\n| Generatable or reverse engineerable personal data? | None |\n| Personal data used to create this model? | None |\n| How often is dataset reviewed? | Before Release |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | Yes |\n| Applicable NVIDIA Privacy Policy | [https://www.nvidia.com/en-us/about-nvidia/privacy-policy/](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) |","safetyAndSecurity","| Field | Response |\n| -- | -- |\n| Model Application(s): | Recognizing Text in Images |\n| Describe the life-critical application (if present). | Not applicable |\n| Use Case Restrictions: | Abide by [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) |\n| Describe access restrictions (if any): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. 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