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Field	Response
Participation considerations from adversely impacted groups (protected classes) in model design and testing:	OCR: Not applicable OCD: Not applicable
Measures taken to mitigate against unwanted bias:	OCR: Not Applicable OCD: Not Applicable

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OCDNet Model Overview

Model Overview

The model described in this card is an optical characters detection network, which aims to detect text in images. Trainable and deployable OCDNet models are provided. These are trained on Uber-Text dataset and ICDAR2015 dataset respectively.

Terms of use

License to use these models is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these licenses.

Model Architecture

This model is based on a relatively sophisticated text detection network called DBNet. DBNet is a network architecture for real-time scene text detection with differentiable binarization. It aims to solve the problem of text localization and segmentation in natural images with complex backgrounds and various text shapes.

Training

The training algorithm inserts the binarization operation into the segmentation network and jointly optimizes it so that\nthe network can learn to separate foreground and background pixels more effectively. The binarization threshold is learned by minimizing the IoU loss between the predicted binary map and the ground truth binary map.

Training Data

The trainable models were trained on the Uber-Text dataset and ICDAR2015 dataset respectively. The Uber-Text dataset contains street-level images collected from car mounted sensors and truths annotated by a team of image analysts--including train_4Kx4K, train_1Kx1K, val_4Kx4K, val_1Kx1K, test_4Kx4K as the training datasets and test_1Kx1K as the validation dataset. The dataset was constructed with 107812 images for training and 10157 images for validation. The ICDAR2015 dataset contains 1000 training images and 500 test images. The deployable models were ONNX models that were exported using the trainable models.

Performance

Evaluation Data

The OCDNet model was evaluated using the Uber-Text test dataset and ICDAR2015 test dataset.

Methodology and KPI

The key performance indicator is the hmean of detection. The KPI 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

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model	test dataset	hmean
ocdnet_deformable_resnet18	Uber-Text	81.1%
ocdnet_deformable_resnet50	Uber-Text	82.2%
ocdnet_fan_tiny_2x_ubertext.pth	Uber-Text	86.0%
ocdnet_fan_tiny_2x_icdar.pth	ICDAR2015	85.3%
ocdnet_fan_tiny_2x_icdar_pruned.pth	ICDAR2015	84.8%
ocdnet_vit_pcb.pth	Internal PCB validation	69.3%

Real-time Inference Performance

The inference uses FP16 precision. The input shape is <batch>x3x640x640. The inference performance runs against an OCDNet-deployable model with trtexec on AGX Orin, Orin NX, Orin Nano, NVIDIA L4, NVIDIA L4, and NVIDIA A100 GPUs. The Jetson devices run at Max-N configuration for maximum system performance. The data is for inference-only performance. The end-to-end performance with streaming video data might vary slightly depending on the application's use case.

\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\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\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

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Model	Device	precision	batch_size	FPS
ocdnet_deformable_resnet18	Orin Nano	FP16	32	31
ocdnet_deformable_resnet18	Orin NX	FP16	32	46
ocdnet_deformable_resnet18	AGX Orin	FP16	32	122
ocdnet_deformable_resnet18	T4	FP16	32	294
ocdnet_deformable_resnet18	L4	FP16	32	432
ocdnet_deformable_resnet18	A100	FP16	32	1786
ocdnet_fan_tiny_2x_icdar	Orin Nano	FP16	1	0.57
ocdnet_fan_tiny_2x_icdar	AGX Orin	FP16	1	2.24
ocdnet_fan_tiny_2x_icdar	T4	FP16	1	2.74
ocdnet_fan_tiny_2x_icdar	L4	FP16	1	5.36
ocdnet_fan_tiny_2x_icdar	A30	FP16	1	8.34
ocdnet_fan_tiny_2x_icdar	L40	FP16	1	15.01
ocdnet_fan_tiny_2x_icdar	A100-sxm4-80gb	FP16	1	16.61
ocdnet_fan_tiny_2x_icdar	H100-sxm-80gb-hbm3	FP16	1	29.13
ocdnet_fan_tiny_2x_icdar_pruned	Orin Nano	FP16	2	0.79
ocdnet_fan_tiny_2x_icdar_pruned	Orin NX	FP16	2	1.18
ocdnet_fan_tiny_2x_icdar_pruned	AGX Orin	FP16	2	3.08
ocdnet_fan_tiny_2x_icdar_pruned	A2	FP16	1	2.30
ocdnet_fan_tiny_2x_icdar_pruned	T4	FP16	2	3.51
ocdnet_fan_tiny_2x_icdar_pruned	L4	FP16	1	7.23
ocdnet_fan_tiny_2x_icdar_pruned	A30	FP16	2	11.37
ocdnet_fan_tiny_2x_icdar_pruned	L40	FP16	2	19.04
ocdnet_fan_tiny_2x_icdar_pruned	A100-sxm4-80gb	FP16	2	22.66
ocdnet_fan_tiny_2x_icdar_pruned	H100-sxm-80gb-hbm3	FP16	2	40.07

How to Use This Model

This model needs to be used with NVIDIA Hardware and Software: The model can run on any NVIDIA GPU, including NVIDIA Jetson devices, with TAO Toolkit, DeepStream SDK or TensorRT.

The primary use case for this model is to detect text on images.

There are two types of models provided (both unpruned).

trainable
deployable

The trainable models are intended for training with the user's own dataset using TAO Toolkit. This can provide high-fidelity models that are adapted to the use case. A Jupyter notebook is available as a part of the TAO container and can be used to re-train.

The deployable models share the same structure as the trainable model, but in onnx format. The deployable models can be deployed using TensorRT, nvOCDR, and DeepStream.

Input

Images of C x H x W (H and W should be multiples of 32.)

Output

BBox or polygon coordinates for each detected text in the input image

Instructions to Use the Model with TAO

To use these models as pretrained weights for transfer learning, use the snippet below as a template for the model component of the experiment spec file to train an OCDNet model. For more information on the experiment spec file, refer to the TAO Toolkit User Guide.

To use trainable_resnet18_v1.0 model:

model:\n  load_pruned_graph: False\n  pruned_graph_path: '/results/prune/pruned_0.1.pth'\n  pretrained_model_path: '/data/ocdnet/ocdnet_deformable_resnet18.pth'\n  backbone: deformable_resnet18\n

To use trainable_ocdnet_vit_v1.0 model:

model:\n  load_pruned_graph: False\n  pruned_graph_path: '/results/prune/pruned_0.1.pth'\n  pretrained_model_path: '/data/ocdnet/ocdnet_fan_tiny_2x_icdar.pth'\n  backbone: fan_tiny_8_p4_hybrid\n  enlarge_feature_map_size: True\n  activation_checkpoint: True\n

Instructions to deploy the model with DeepStream

To create the entire end-to-end video analytic application, deploy this model with DeepStream SDK. DeepStream SDK is a streaming analytic toolkit to accelerate building AI-based video analytic applications. DeepStream supports direct integration of this model into the Deepstream sample app.

To deploy this model with DeepStream, follow these instructions.

Limitations

Restricted Usage in Different Fields

The NVIDIA OCDNet trainable model is trained on Uber Text, ICDAR2015 and PCB text dataset, which contains street-view images only. To get better accuracy in a specific field, more data is usually required to fine tune the pre-trained model with TAO Toolkit.

Model versions:

trainable_resnet18_v1.0 - Pre-trained models with deformable-resnet18 backbone, trained on Uber-Text dataset.
trainable_resnet50_v1.0 - Pre-trained models with deformable-resnet50 backbone, trained on Uber-Text dataset.
trainable_ocdnet_vit_v1.0 - Pre-trained models with fan-tiny backbone, trained on ICDAR2015 dataset.
trainable_ocdnet_vit_v1.1 - Pre-trained models with fan-tiny backbone, trained on Uber-Text dataset.
trainable_ocdnet_vit_v1.2 - Pre-trained models with fan-tiny backbone, trained on PCB dataset.
trainable_ocdnet_vit_v1.3 - Pre-trained models with fan-tiny backbone, trained on ImageNet2012 dataset.
trainable_ocdnet_vit_v1.4 - Pre-trained models with fan-tiny backbone, trained on ICDAR2015 dataset and model are pruned.
deployable_v1.0 - Model deployable with deformable-resnet backbone.
deployable_v2.0 - Model deployable with fan-tiny backbone, trained on ICDAR2015.
deployable_v2.1 - Model deployable with fan-tiny backbone, trained on Uber-Text.
deployable_v2.2 - Model deployable with fan-tiny backbone, trained on PCB dataset.
deployable_v2.3 - Model deployable with fan-tiny backbone, trained on ICDAR2015 and model are pruned.

Reference

Citations

Liao M., Wan Z., Yao C., Chen K., Bai X.: Real-time Scene Text Detection with Differentiable Binarization (2020).
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y: Deformable convolutional networks. (2017).
He, W., Zhang, X., Yin, F., and Liu, C.: Deep direct regression for multi-oriented scene text detection. (2017).
Zhang, Y., Gueguen, L., Zharkov, I., Zhang, P., Seifert, K., & Kadlec, B. (2017, July). Uber-text: A large-scale dataset for optical character recognition from street-level imagery. In SUNw: Scene Understanding Workshop-CVPR (Vol. 2017, p. 5).
Zhou, D., Yu, Z., Xie, E., Xiao, C., Anandkumar, A., Feng, J., & Alvarez, J. M. (2022, June). Understanding the robustness in vision transformers. In International Conference on Machine Learning (pp. 27378-27394). PMLR.
Kuo, C. W., Ashmore, J. D., Huggins, D., & Kira, Z. (2019, January). Data-efficient graph embedding learning for PCB component detection. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 551-560). IEEE.

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

License

The license to use these models is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these licenses.

Technical Blogs

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 traina real-time License plate detection and recognition app with TAO and DeepStream.
Model accuracy is extremely important. Learn how to achieve state-of-the-art accuracy for classification and object detection models using TAO.

Ethical AI

The NVIDIA OCDNet model detects optical characters.

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developers to ensure that it meets the requirements for the relevant industry and use case, that the necessary instructions and documentation are provided to understand error rates, confidence intervals, and results, and that the model is being used under the conditions and in the manner intended.

Please report security vulnerabilities or NVIDIA AI Concerns here

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Trainable and deployable OCDNet models are provided. These are trained on Uber-Text dataset and ICDAR2015 dataset respectively.\n\n## Terms of use \nLicense to use these models is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these [licenses](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/).\n\n## Model Architecture \n\nThis model is based on a relatively sophisticated text detection network called DBNet. DBNet is a network architecture for real-time scene text detection with differentiable binarization. It aims to solve the problem of text localization and segmentation in natural images with complex backgrounds and various text shapes.\n\n## Training \n\nThe training algorithm inserts the binarization operation into the segmentation network and jointly optimizes it so that\nthe network can learn to separate foreground and background pixels more effectively. The binarization threshold is learned by minimizing the IoU loss between the predicted binary map and the ground truth binary map.\n\n\n### Training Data \n\nThe trainable models were trained on the [Uber-Text](https://s3-us-west-2.amazonaws.com/uber-common-public/ubertext/index.html) dataset and [ICDAR2015](https://rrc.cvc.uab.es/?ch=4) dataset respectively. The Uber-Text dataset contains street-level images collected from car mounted sensors and truths annotated by a team of image analysts--including train_4Kx4K, train_1Kx1K, val_4Kx4K, val_1Kx1K, test_4Kx4K as the training datasets and test_1Kx1K as the validation dataset. The dataset was constructed with 107812 images for training and 10157 images for validation. The ICDAR2015 dataset contains 1000 training images and 500 test images. The deployable models were ONNX models that were exported using the trainable models.\n\n## Performance \n\n### Evaluation Data \n\nThe OCDNet model was evaluated using the Uber-Text test dataset and ICDAR2015 test dataset.\n\n### Methodology and KPI\n\nThe key performance indicator is the hmean of detection. The KPI for the evaluation data are reported below.\n\n|model|test dataset|hmean|\n|---|---|---|\n|ocdnet_deformable_resnet18|Uber-Text|81.1%|\n|ocdnet_deformable_resnet50|Uber-Text|82.2%|\n|ocdnet_fan_tiny_2x_ubertext.pth|Uber-Text|86.0%|\n|ocdnet_fan_tiny_2x_icdar.pth|ICDAR2015|85.3%|\n|ocdnet_fan_tiny_2x_icdar_pruned.pth|ICDAR2015|84.8%|\n|ocdnet_vit_pcb.pth|Internal PCB validation|69.3%|\n\n\n### Real-time Inference Performance \n\nThe inference uses FP16 precision. The input shape is `x3x640x640`. The inference performance runs against an OCDNet-deployable model with [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/master/samples/trtexec) on AGX Orin, Orin NX, Orin Nano, NVIDIA L4, NVIDIA L4, and NVIDIA A100 GPUs. The Jetson devices run at Max-N configuration for maximum system performance. The data is for inference-only performance. The end-to-end performance with streaming video data might vary slightly depending on the application's use case.\n\n|Model|Device|precision|batch_size|FPS|\n|---|---|---|---|---|\n|ocdnet_deformable_resnet18|Orin Nano|FP16|32|31|\n|ocdnet_deformable_resnet18|Orin NX|FP16|32|46|\n|ocdnet_deformable_resnet18|AGX Orin|FP16|32|122|\n|ocdnet_deformable_resnet18|T4|FP16|32|294|\n|ocdnet_deformable_resnet18|L4|FP16|32|432|\n|ocdnet_deformable_resnet18|A100|FP16|32|1786|\n|ocdnet_fan_tiny_2x_icdar|Orin Nano|FP16|1|0.57|\n|ocdnet_fan_tiny_2x_icdar|AGX Orin|FP16|1|2.24|\n|ocdnet_fan_tiny_2x_icdar|T4|FP16|1|2.74|\n|ocdnet_fan_tiny_2x_icdar|L4|FP16|1|5.36|\n|ocdnet_fan_tiny_2x_icdar|A30|FP16|1|8.34|\n|ocdnet_fan_tiny_2x_icdar|L40|FP16|1|15.01|\n|ocdnet_fan_tiny_2x_icdar|A100-sxm4-80gb|FP16|1|16.61|\n|ocdnet_fan_tiny_2x_icdar|H100-sxm-80gb-hbm3|FP16|1|29.13|\n|ocdnet_fan_tiny_2x_icdar_pruned|Orin Nano|FP16|2|0.79|\n|ocdnet_fan_tiny_2x_icdar_pruned|Orin NX|FP16|2|1.18|\n|ocdnet_fan_tiny_2x_icdar_pruned|AGX Orin|FP16|2|3.08|\n|ocdnet_fan_tiny_2x_icdar_pruned|A2|FP16|1|2.30|\n|ocdnet_fan_tiny_2x_icdar_pruned|T4|FP16|2|3.51|\n|ocdnet_fan_tiny_2x_icdar_pruned|L4|FP16|1|7.23|\n|ocdnet_fan_tiny_2x_icdar_pruned|A30|FP16|2|11.37|\n|ocdnet_fan_tiny_2x_icdar_pruned|L40|FP16|2|19.04|\n|ocdnet_fan_tiny_2x_icdar_pruned|A100-sxm4-80gb|FP16|2|22.66|\n|ocdnet_fan_tiny_2x_icdar_pruned|H100-sxm-80gb-hbm3|FP16|2|40.07|\n\n\n## How to Use This Model \n\nThis model needs to be used with NVIDIA Hardware and Software: The model can run on any NVIDIA GPU, including NVIDIA Jetson devices, with [TAO Toolkit](https://developer.nvidia.com/tao-toolkit), [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk) or [TensorRT](https://developer.nvidia.com/tensorrt).\n\nThe primary use case for this model is to detect text on images.\n\nThere are two types of models provided (both unpruned).\n\n- trainable\n- deployable\n\nThe `trainable` models are intended for training with the user's own dataset using TAO Toolkit. This can provide high-fidelity models that are adapted to the use case. A Jupyter notebook is available as a part of the [TAO container](https://ngc.nvidia.com/catalog/containers/nvidia:tao:tao-toolkit) and can be used to re-train.\n\nThe `deployable` models share the same structure as the `trainable` model, but in `onnx` format. The `deployable` models can be deployed using TensorRT, nvOCDR, and DeepStream.\n\n\n### Input\n\nImages of C x H x W (H and W should be multiples of 32.)\n\n### Output\n\nBBox or polygon coordinates for each detected text in the input image\n\n### Instructions to Use the Model with TAO\n\nTo use these models as pretrained weights for transfer learning, use the snippet below as a template for the `model` component of the experiment spec file to train an OCDNet model. For more information on the experiment spec file, refer to the [TAO Toolkit User Guide](https://docs.nvidia.com/tao/tao-toolkit/index.html).\n\nTo use trainable_resnet18_v1.0 model:\n```yaml\nmodel:\n load_pruned_graph: False\n pruned_graph_path: '/results/prune/pruned_0.1.pth'\n pretrained_model_path: '/data/ocdnet/ocdnet_deformable_resnet18.pth'\n backbone: deformable_resnet18\n```\nTo use trainable_ocdnet_vit_v1.0 model:\n```yaml\nmodel:\n load_pruned_graph: False\n pruned_graph_path: '/results/prune/pruned_0.1.pth'\n pretrained_model_path: '/data/ocdnet/ocdnet_fan_tiny_2x_icdar.pth'\n backbone: fan_tiny_8_p4_hybrid\n enlarge_feature_map_size: True\n activation_checkpoint: True\n```\n\n### Instructions to deploy the model with DeepStream\n\nTo create the entire end-to-end video analytic application, deploy this model with [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk). DeepStream SDK is a streaming analytic toolkit to accelerate building AI-based video analytic applications. DeepStream supports direct integration of this model into the Deepstream sample app.\n\nTo deploy this model with [DeepStream](https://developer.nvidia.com/deepstream-sdk), follow [these instructions](https://docs.nvidia.com/tao/tao-toolkit/text/ds_tao/nvocdr_ds.html).\n\n## Limitations \n\n### Restricted Usage in Different Fields\n\nThe NVIDIA OCDNet trainable model is trained on Uber Text, ICDAR2015 and PCB text dataset, which contains street-view images only. To get better accuracy in a specific field, more data is usually required to fine tune the pre-trained model with TAO Toolkit.\n\n## Model versions:\n\n- **trainable_resnet18_v1.0** - Pre-trained models with deformable-resnet18 backbone, trained on Uber-Text dataset.\n- **trainable_resnet50_v1.0** - Pre-trained models with deformable-resnet50 backbone, trained on Uber-Text dataset.\n- **trainable_ocdnet_vit_v1.0** - Pre-trained models with fan-tiny backbone, trained on ICDAR2015 dataset.\n- **trainable_ocdnet_vit_v1.1** - Pre-trained models with fan-tiny backbone, trained on Uber-Text dataset.\n- **trainable_ocdnet_vit_v1.2** - Pre-trained models with fan-tiny backbone, trained on PCB dataset.\n- **trainable_ocdnet_vit_v1.3** - Pre-trained models with fan-tiny backbone, trained on ImageNet2012 dataset.\n- **trainable_ocdnet_vit_v1.4** - Pre-trained models with fan-tiny backbone, trained on ICDAR2015 dataset and model are pruned.\n- **deployable_v1.0** - Model deployable with deformable-resnet backbone.\n- **deployable_v2.0** - Model deployable with fan-tiny backbone, trained on ICDAR2015.\n- **deployable_v2.1** - Model deployable with fan-tiny backbone, trained on Uber-Text.\n- **deployable_v2.2** - Model deployable with fan-tiny backbone, trained on PCB dataset.\n- **deployable_v2.3** - Model deployable with fan-tiny backbone, trained on ICDAR2015 and model are pruned.\n\n## Reference\n\n### Citations \n\n- Liao M., Wan Z., Yao C., Chen K., Bai X.: Real-time Scene Text Detection with Differentiable Binarization (2020).\n- Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y: Deformable convolutional networks. (2017).\n- He, W., Zhang, X., Yin, F., and Liu, C.: Deep direct regression for multi-oriented scene text detection. (2017).\n- Zhang, Y., Gueguen, L., Zharkov, I., Zhang, P., Seifert, K., & Kadlec, B. (2017, July). Uber-text: A large-scale dataset for optical character recognition from street-level imagery. In SUNw: Scene Understanding Workshop-CVPR (Vol. 2017, p. 5).\n- Zhou, D., Yu, Z., Xie, E., Xiao, C., Anandkumar, A., Feng, J., & Alvarez, J. M. (2022, June). Understanding the robustness in vision transformers. In International Conference on Machine Learning (pp. 27378-27394). PMLR.\n- Kuo, C. W., Ashmore, J. D., Huggins, D., & Kira, Z. (2019, January). Data-efficient graph embedding learning for PCB component detection. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 551-560). IEEE.\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## License \n\nThe license to use these models is covered by the Model EULA. By downloading the unpruned or pruned version of the model, you accept the terms and conditions of these [licenses](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/).\n\n## Technical Blogs \n\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 traina [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 to 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\n## Suggested Reading \n\n- More information 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 Toolkit Quick Start Guide](https://docs.nvidia.com/tao/tao-toolkit/text/tao_toolkit_quick_start_guide.html) 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 the [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 the [DeepStream SDK](https://developer.nvidia.com/deepstream-sdk).\n\n## Ethical AI \n\nThe NVIDIA OCDNet model detects optical characters.\n\nNVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developers to ensure that it meets the requirements for the relevant industry and use case, that the necessary instructions and documentation are provided to understand error rates, confidence intervals, and results, and that the model is being used under the conditions and in the manner intended.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability)","displayName","Optical Character Detection",["D",1732745336639],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/ocdnet","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO.png","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: | OCR: Not applicable
OCD: Not applicable |\n| Measures taken to mitigate against unwanted bias: | OCR: Not Applicable
OCD: Not Applicable |","explainability","| Field | Response |\n| -- | -- |\n| Intended Application(s) & Domain(s): | OCR: This model is intended to be used in computer vision application for recognizing the optical characters in a scene.
OCD: This model is intended to be used for detecting text in images. |\n| Model Type: | OCR: This model is intended for developers who want to recognize/identify text from image data.
OCD: This model is intended for developers who want to detect optical characters from image data. |\n| Intended Users: | OCR: This model is intended for developers who want to customize optical character recognition models.
OCD: This model is intended to be used for detecting text in images. |\n| Output: | OCR: Sequence of characters
OCD: BBox or polygon coordinates for each detected text in the input image |\n| Describe how the model works: | OCR: The training algorithm minimizes the connectionist temporal classification (CTC) loss between a ground truth character sequence from the image and a predicted characters sequence. Characters are decoded from the sequence output by a best-path decoding method.
OCD: Based on DBNet, a network architecture for real-time scene text detection, this model aims to solve the problem of text localization and segmentation in natural images with complex backgrounds and various text shapes. |\n| Technical Limitations: | OCR: This model performs best on images representing its training set: Uber Text and TextOCR. Uber Text contains street view images. TextOCR has images with text in various scenes. Further fine-tuning might be required for domain-specific accuracy.
OCD: The NVIDIA OCDNet trainable model is trained on Uber Text, which contains street-view images only. Further fine-tuning might be required for domain-specific accuracy. |\n| Verified to have met prescribed NVIDIA standards: | OCR: Yes
OCD: Yes |\n| Performance Metrics: | OCR: Accuracy
OCD: hmean |\n| Potential Risks: | OCR: None Known
OCR: None Known |\n| Licensing: | [https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/) |","privacy","| Field | Response |\n| -- | -- |\n| Generatable or reverse engineerable personally-identifiable information (PII)? | OCR: Neither
OCD: Neither |\n| If applicable, was a notice provided to the individuals prior to the collection of any personal data used? | OCR: No PII was used
OCD: Not applicable |\n| How often is the dataset reviewed? | OCR: Before Release
OCD: Before Release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | OCR: No
OCD: No |\n| If PII was collected for the development of the model, was it collected directly by NVIDIA? | OCR: No PII was collected
OCD: No PII was collected |\n| If PII was collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | OCR: No PII was collected
OCD: No PII was collected |\n| If PII collected for the development of this AI model, was it minimized to only what was required? | OCR: No
OCD: No |\n| Is there provenance for all datasets used in training? | OCR: No
OCD: No |\n| Does data labeling (annotation, metadata) comply with privacy laws? | OCR: Yes
OCD: Yes|\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | OCR: No, not possible with externally-sourced data.
OCD: No, not possible with externally-sourced data.|\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): | OCD: Detect visual regions containing text
OCR: Recognize the text in visual data |\n| Describe the life-critical application (if present). | OCR: No physical safety impacts.
OCD: No physical safety impacts. |\n| Use Case Restrictions: | OCD: Images that do not contain text
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