Boost OCR Model Performance: Train with Custom License Plate Data

Table of Contents

1. Introduction
2. The Need for Custom Data Set in Computer Vision
3. Understanding OCR (Optical Character Recognition)
4. Limitations of Computer Vision Models for OCR
5. Benefits of Creating Custom Data Sets for License Plate Recognition
6. Getting Started: Signing up for Tios
7. Exploring the Library of AI Subfields in Tios
8. Creating and Uploading Custom Data Sets in Tios
9. Labeling Images in Tios for License Plates
10. Training the Object Detection Model in Tios
11. Deploying and testing the Object Detection Model
12. Training the Padelo CR Model for Text Recognition
13. Combining Xolo V7 Tiny with Padelo CR for Text Recognition
14. Conclusion

The Need for Custom Data Set in Computer Vision

In the field of computer vision, the popularity of optical character recognition (OCR) models is on the rise. These models are widely used for tasks such as text detection and recognition in images. While these models perform well and are fast on CPUs, they may encounter challenges in specific cases, such as license plate recognition. To address this issue and improve the performance of OCR models, creating a custom data set of license plates can make a significant difference. By training the models with your own data set, you can achieve better results and accuracy.

Understanding OCR (Optical Character Recognition)

OCR, or optical character recognition, is a technology used to recognize and extract text from images. This technology has various applications, including document scanning, automated data entry, and license plate recognition. OCR models are trained to detect and interpret text in images, making it possible to extract valuable information from visual data. By leveraging OCR, businesses and organizations can automate processes, improve efficiency, and extract textual information for further analysis.

Limitations of Computer Vision Models for OCR

Although computer vision models for OCR are effective in many cases, they have limitations when it comes to tasks like license plate recognition. In some situations, these models may not perform well, leading to inaccurate or unreliable results. This can be particularly problematic for applications where accuracy is crucial, such as traffic monitoring systems or parking management. To overcome these limitations, creating a custom data set specifically designed for license plate recognition can significantly enhance the performance of OCR models.

Benefits of Creating Custom Data Sets for License Plate Recognition

By creating a custom data set for license plate recognition, you can reap multiple benefits. Firstly, a custom data set allows you to train the OCR models using images that closely Resemble the real-world scenarios in which the models will be deployed. This ensures that the models are capable of accurately detecting and recognizing license plates in various environments and lighting conditions. Secondly, custom data sets enable you to fine-tune the models based on your specific use case, enhancing their accuracy and reliability. This customization is crucial for applications where a high level of precision is required.

Getting Started: Signing up for Tios

To create and label your own custom data sets for OCR and train the models, you can use Tios, a platform specifically designed for AI and computer vision tasks. The first step is to sign up for Tios by clicking on the "Sign up" button on the website. Fill in the simple form to create a free account and receive your login details. These login details will be used to access Tios and follow the Tutorial.

Exploring the Library of AI Subfields in Tios

Once you log into Tios, you will be directed to the library section, where you can explore various subfields of AI and computer vision. Tios offers options such as object detection, OCR, sentiment classification, language processing, and Speech Recognition. In this tutorial, we will focus on object detection and OCR. These two models will be used to accomplish license plate recognition. By leveraging the power of both models, we can achieve accurate text detection and recognition.

Creating and Uploading Custom Data Sets in Tios

To train the OCR models, you need to create a custom data set of license plates in Tios. In the data section of Tios, click on the "+" button to create a new data set. Confirm the creation, and you will be prompted to upload your data set. In this tutorial, we will use the Syntax data set, which contains 16,000 images of license plates. Tios offers a free plan that allows you to upload up to 1000 images. If you want to use the full data set, you can choose an affordable pricing plan that suits your needs.

To upload the data set, simply unzip the file containing the images and drag and drop the entire folder into Tios. Click on "Start upload" to initiate the upload process. Once the upload is complete, you will be able to see the data set in Tios.

Labeling Images in Tios for License Plates

To train the object detection model, you need to label the images in your data set. In Tios, go to the labeling section and select the data set you created. You will see the images from your data set displayed for labeling. Use the annotation tools to draw bounding boxes around the license plates in each image. Additionally, input the text contained within each bounding box into the corresponding text input box. This process helps the models learn the location and content of license plates. Repeat this process for all images in your data set.

When creating a custom data set, you may encounter difficulties training the object detection model, especially if you aim for high accuracy. To ensure optimal performance, it is advisable to label a significant number of images to improve the model's accuracy. While this may be time-consuming, the added effort will significantly enhance the final performance of the model.

Training the Object Detection Model in Tios

Once you have labeled your custom data set, you can proceed to train the object detection model. In the training section of Tios, create a new training session specifically for scene text detection. Select the Syntax data set you created and choose the "Object Detection" algorithm. In this tutorial, we will be using the Xolo V7 Tiny model for object detection.

After selecting the algorithm, you must specify the machine to be used for neural network training. If you have a GPU, it is recommended to use it as it significantly speeds up the training process. Tios provides a cloud machine option for users who do not have GPUs available. However, if you have a paid plan with Tios, you can utilize their cloud machines that offer consistent connectivity and ensure uninterrupted training Sessions.

Once you have configured the training session details, such as the batch size and initial weights, click on "Start training" to initiate the training process. Tios will begin creating the dataset manifest, which serves as a foundation for the training session. It downloads all the images and labels and proceeds to train the model. During the training process, you will be able to monitor the training metrics, such as accuracy and loss, in real-time.

Deploying and Testing the Object Detection Model

After training the object detection model, you can deploy and test it using the Tios platform. In the deploy section of Tios, create a new deployment using the weights obtained from the training session. This deployment will allow you to utilize the trained model and test its performance.

To test the model, you can use the Tios playground. Simply paste the URL of an image containing license plates and click on "Detect" to initiate the detection process. The model will accurately detect the bounding boxes around the license plates in the image. You can also view the JSON output for further analysis.

Training the Padelo CR Model for Text Recognition

To recognize the text inside the bounding boxes detected by the object detection model, we need to train the Padelo CR model. In the training section of Tios, create a new training session specifically for scene text OCR (Optical Character Recognition). Select the Syntax data set and choose the OCR algorithm. In this tutorial, we will be using the Padelo CR model for text recognition.

Similar to the object detection model, you need to specify the machine to be used for training the OCR model. Ensure that this machine has a GPU to optimize the training process. If you are using a Colab instance, make sure to connect it to Tios using the provided instructions. Alternatively, you can use the cloud machines provided by Tios, ensuring uninterrupted training sessions.

Configure the training session details, such as the initial weights, and start the training process. Monitor the training metrics and wait for the training to complete.

Combining Xolo V7 Tiny with Padelo CR for Text Recognition

To perform accurate text recognition, it is necessary to combine the output of the object detection model (Xolo V7 Tiny) with the text recognition model (Padelo CR). This can be done using the Easy Xolo V7 Package and the Easy Padelo CR repository. The process involves detecting the bounding boxes around the text using Xolo V7 Tiny and passing these cropped images to the Padelo CR model for text recognition.

Using the provided code, load the Xolo V7 Tiny model and initialize the Padelo CR text recognizer with the custom weights obtained from Tios. Then, load the image using the OpenCV library and pass it to the text recognizer to obtain the prediction for the text content. This combined approach ensures accurate and efficient text recognition.

By training the object detection model and the text recognition model separately and then combining them, you can achieve highly accurate license plate recognition.

Conclusion

In conclusion, creating a custom data set for license plate recognition can significantly enhance the performance of OCR models. By following the steps outlined in this tutorial, you can leverage Tios to create, label, and train custom data sets for OCR tasks. Additionally, combining object detection models with text recognition models enables accurate license plate recognition. With the ability to customize and fine-tune these models, you can achieve high accuracy and reliability in license plate recognition applications.

Now that you have the knowledge and tools to create your own custom data sets and train OCR models, you can embark on projects that require accurate text detection and recognition. Whether it's for traffic monitoring, parking management, or document scanning, OCR models trained with custom data sets will greatly improve the accuracy and reliability of your applications.