Models
Optical Character Recognition

Optical Character Recognition

Logo for Optical Character Recognition
Description
Model to recognise characters from a preceding OCDNet model.
Publisher
NVIDIA
Latest Version
deployable_v2.1.1
Modified
September 20, 2024
Size
44.27 MB

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)
Network Architecture: ResNet50
Model 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
Input Format: Gray Image
Input Parameters: 3D
Other 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
Output Format: Character Id sequence: Text String(s)
Other Properties Related to Output: None

Software Integration:

Runtime(s): NVIDIA AI Enterprise
Toolkit: TAO Framework
Supported Hardware Platform(s): Ampere, Jetson, Hopper, Lovelace, Pascal, Turing
Supported 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
Test 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 the 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.

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.

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

To use trainable_ocdnet_vit_v1.0 model:

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

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

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

Suggested Reading

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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