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This model card combines the relevant information of OCR and OCD models

OCRNet Model Overview

Description

Optical character recognition network recognizes characters from the gray images.

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.

References(s):

Citations

Baek, J., Kim, G., Lee, J., Park, S., Han, D., Yun, S., ... & Lee, H. (2019). What is wrong with scene text recognition model comparisons? dataset and model analysis. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 4715-4723).
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).
Singh, A., Pang, G., Toh, M., Huang, J., Galuba, W., & Hassner, T. (2021). Textocr: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 8802-8812)
Graves, Alex, et al. \"Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks.\" In: Proceedings of the 23rd international conference on Machine learning (2006)
He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. In: CVPR (2015)
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.

Model Architecture:

Architecture Type: Convolution Neural Network (CNN)
\nNetwork Architecture: ResNet50
\nModel Version:

trainable_v1.0 - Pre-trained model with ResNet backbone on scene text.
deployable_v1.0 - Models deployable with ResNet backbone.
trainable_v2.0 - Pre-trained model with FAN backbone on scene text.
deployable_v2.0 - Model deployable with FAN backbone on scene text.
trainable_v2.1 - Pre-trained model with FAN backbone on PCB text.
deployable_v2.1 - Model deployable with FAN backbone on PCB text.

Input:

Input Type(s): Image
\nInput Format: Gray Image
\nInput Parameters: 3D
\nOther Properties Related to Input:

Gray Images of 1 X 32 X 100 (C H W) for trainable_v1.0/deployable_v1.0
Gray Images of 1 X 64 X 200 (C H W) for trainable_v2.0/trainable_v2.1/deployable_v2.0/deployable_v2.1

Output:

Output Type(s): Sequence of characters
\nOutput Format: Character Id sequence: Text String(s)
\nOther Properties Related to Output: None

Software Integration:

Runtime(s): NVIDIA AI Enterprise
\nToolkit: TAO Framework
\nSupported Hardware Platform(s): Ampere, Jetson, Hopper, Lovelace, Pascal, Turing
\nSupported Operating System(s): Linux

Training & Finetuning:

Dataset:

OCRNet pretrained model was trained on Uber-Text and TextOCR dataset. The Uber-Text contains street-level images collected from car mounted sensors and truths annotated by a team of image analysts. The TextOCR is the images with annotated texts from OpenImages dataset. After collecting the original data from Uber-text and TextOCR, we remove all the text images with * label in Uber-text and only keep alphanumeric text images with the maximum length is 25 in both datasets. We finally construct the dataset with 805007 text images for training and 24388 images for validation.

Inference:

Engine: TensorRT
\nTest Hardware:

Orin Nano
Orin NX
AGX Orin
L4
L40
T4
A2
A30
A100
H100

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

We test the OCRNet model on three different datasets: Uber-Text + TextOCR validation dataset, ICDAR13 and ICDAR15 scene text benchmark dataset.

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 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\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
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%

Real-time Inference Performance

The inference uses FP16 precision. The inference performance runs with trtexec on AGX Orin, Orin NX, Orin Nano and NVIDIA L4 GPU. The Jetson devices run at Max-N configuration for maximum system performance. The data is the inference only performance. The end-to-end performance with streaming video data might slightly vary depending on use cases of applications.

\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

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

Model	Device	precision	batch_size	FPS
ocrnet_resnet50_pruned	Orin Nano	FP16	128	981
ocrnet_resnet50_pruned	Orin NX	FP16	128	1399
ocrnet_resnet50_pruned	AGX Orin	FP16	128	3921
ocrnet_resnet50_pruned	L4	FP16	512	6404
ocrnet-vit	Orin Nano	FP16+INT8	4	100
ocrnet-vit	Orin NX	FP16+INT8	4	147
ocrnet-vit	AGX Orin	FP16+INT8	8	393
ocrnet-vit	T4	FP32	16	680
ocrnet-vit	A2	FP32	32	428
ocrnet-vit	A30	FP32	32	1645
ocrnet-vit	L4	FP32	32	1471
ocrnet-vit	L40	FP32	32	4320
ocrnet-vit	A100	FP32	32	2513
ocrnet-vit	H100	FP32	32	3846

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.

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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.

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\n**Network Architecture:** ResNet50
\n**Model Version:**
\n- **trainable_v1.0** - Pre-trained model with ResNet backbone on scene text.\n- **deployable_v1.0** - Models deployable with ResNet backbone.\n- **trainable_v2.0** - Pre-trained model with FAN backbone on scene text.\n- **deployable_v2.0** - Model deployable with FAN backbone on scene text.\n- **trainable_v2.1** - Pre-trained model with FAN backbone on PCB text.\n- **deployable_v2.1** - Model deployable with FAN backbone on PCB text.\n\n## Input: \n**Input Type(s):** Image
\n**Input Format:** Gray Image
\n**Input Parameters:** 3D
\n**Other Properties Related to Input:**
\n - Gray Images of 1 X 32 X 100 (C H W) for trainable_v1.0/deployable_v1.0\n - Gray Images of 1 X 64 X 200 (C H W) for trainable_v2.0/trainable_v2.1/deployable_v2.0/deployable_v2.1\n\n## Output: \n**Output Type(s):** Sequence of characters
\n**Output Format:** Character Id sequence: Text String(s)
\n**Other Properties Related to Output:** None
\n\n## Software Integration: \n**Runtime(s):** NVIDIA AI Enterprise
\n**Toolkit:** TAO Framework
\n**Supported Hardware Platform(s):** Ampere, Jetson, Hopper, Lovelace, Pascal, Turing
\n**Supported Operating System(s):** Linux
\n\n# Training & Finetuning:\n\n## Dataset: \n\nOCRNet pretrained model was trained on Uber-Text and TextOCR dataset. The Uber-Text contains street-level images collected from car mounted sensors and truths annotated by a team of image analysts. The TextOCR is the images with annotated texts from OpenImages dataset. After collecting the original data from Uber-text and TextOCR, we remove all the text images with `*` label in Uber-text and only keep alphanumeric text images with the maximum length is 25 in both datasets. We finally construct the dataset with 805007 text images for training and 24388 images for validation.\n\n## Inference: \n**Engine:** TensorRT
\n**Test Hardware:**
\n- Orin Nano\n- Orin NX\n- AGX Orin\n- L4\n- L40\n- T4\n- A2\n- A30\n- A100\n- H100\n\n# OCDNet Model Overview\n\n## Model Overview \n\nThe 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.\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\nWe test the OCRNet model on three different datasets: Uber-Text + TextOCR validation dataset, ICDAR13 and ICDAR15 scene text benchmark dataset.\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 KPI for the evaluation data are reported below. \n\n|model|dataset|accuracy|\n|---|---|---|\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### Real-time Inference Performance \n\nThe inference uses FP16 precision. The inference performance runs with [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/master/samples/trtexec) on AGX Orin, Orin NX, Orin Nano and NVIDIA L4 GPU. The Jetson devices run at Max-N configuration for maximum system performance. The data is the inference only performance. The end-to-end performance with streaming video data might slightly vary depending on use cases of applications.\n\n|Model|Device|precision|batch_size|FPS|\n|---|---|---|---|---|\n|ocrnet_resnet50_pruned|Orin Nano|FP16|128|981|\n|ocrnet_resnet50_pruned|Orin NX|FP16|128|1399|\n|ocrnet_resnet50_pruned|AGX Orin|FP16|128|3921|\n|ocrnet_resnet50_pruned|L4|FP16|512|6404|\n|ocrnet-vit|Orin Nano|FP16+INT8|4|100|\n|ocrnet-vit|Orin NX|FP16+INT8|4|147|\n|ocrnet-vit|AGX Orin|FP16+INT8|8|393|\n|ocrnet-vit|T4|FP32|16|680|\n|ocrnet-vit|A2|FP32|32|428|\n|ocrnet-vit|A30|FP32|32|1645|\n|ocrnet-vit|L4|FP32|32|1471|\n|ocrnet-vit|L40|FP32|32|4320|\n|ocrnet-vit|A100|FP32|32|2513|\n|ocrnet-vit|H100|FP32|32|3846|\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 Recognition",["D",1732745347085],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/tao/models/ocrnet","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
OCR: Images that do not contain text |\n| Describe access restrictions (if any): | None. This was trained on public datasets. |","featureLabels",[],"displayLabels",[265,266,267,268,269,270,271,272,273,274,275,276,277,278],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_229":316,"_280":317,"_281":318,"_283":284},{"_229":312,"_280":313,"_281":314,"_283":284},{"_229":310,"_280":310,"_281":311,"_283":284},{"_229":308,"_280":308,"_281":309,"_283":284},{"_229":305,"_280":306,"_281":307,"_283":284},{"_229":301,"_280":302,"_281":303,"_283":284},{"_229":299,"_280":299,"_281":300,"_283":284},{"_229":297,"_280":297,"_281":298,"_283":284},{"_229":295,"_280":295,"_281":296,"_283":284},{"_229":293,"_280":293,"_281":294,"_283":284},{"_229":290,"_280":290,"_281":291,"_283":284},{"_229":288,"_280":288,"_281":289,"_283":284},{"_229":286,"_280":286,"_281":287,"_283":284},{"_229":279,"_280":279,"_281":282,"_283":284},"Transfer Learning Toolkit","value","filter",{"_285":284,"_280":279},"category","label","field","Transfer Learning",{"_285":284,"_280":286},"TLT",{"_285":284,"_280":288},"TAO Toolkit",{"_285":292,"_280":290},"framework","TAO",{"_285":284,"_280":293},"Smart Infrastructure",{"_285":284,"_280":295},"Smart City",{"_285":284,"_280":297},"NSPECT-U6E7-WAPV",{"_285":284,"_280":299},"Metropolis","pltfm_metropolis",{"_285":304,"_280":302},"platform","DeepStream","pltfm_deepstream",{"_285":304,"_280":306},"Deep Learning",{"_285":284,"_280":308},"CV",{"_285":284,"_280":310},"Computer Vision","soln_computer_vision",{"_285":315,"_280":313},"technology","AI","soln_ai",{"_285":315,"_280":317},{"_106":1491,"_108":1492},"ngcApps",[1444],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1425":1426,"_1427":1428,"_1429":1430,"_1431":1432,"_1433":330,"_138":354,"_1434":1435,"_1436":55,"_1437":85,"_1438":85,"_1439":1440,"_1441":85,"_1442":1443},"amplitude",{"_1422":1423,"_1026":-7,"_1424":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1416":1417,"_1418":1419,"_1420":1421},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1301":166,"_1302":1303,"_1304":1305,"_1306":1307},"announcements",[],"consentState",{"_393":1291,"_1292":85,"_1293":30,"_1294":30,"_1295":1296},"consentConfig",{"_375":376,"_377":378,"_379":380,"_381":382,"_383":384,"_385":386,"_387":388,"_389":390,"_391":392,"_393":394},"showContactAdmin",1783688292145,[],[],[368,369,370,371,372,373],{"_44":59,"_46":69,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":67,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":101,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":374,"_54":55},5,"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","research.nvidia.com","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","s_pltp","This cookie is typically associated with Adobe Analytics (formerly SiteCatalyst). It is used to identify the previous link clicked within the site, which helps in analyzing site usage patterns.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","This cookie name is used as a long-lived unique identifier of the visitor on the website. 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