End-to-end-for-chinese-plate-recognition

End-to-end-for-Chinese-Plate-Recognition

The "End-to-end-for-Chinese-Plate-Recognition" project is a software solution designed for identifying Chinese license plates, regardless of complex shooting angles, lighting conditions, or potential distortions. This robust system integrates several advanced technologies to ensure precise plate detection and recognition, providing impressive results even in challenging environments.

Technologies Used

The project relies on a combination of the following technologies and frameworks:

• U-Net: Applied for image segmentation to produce binary images that can isolate and detect license plate areas.
• OpenCV (cv2): Utilized for edge detection. It identifies the coordinates of the license plate in the image and helps correct any distortions.
• Convolutional Neural Network (CNN): Employed for end-to-end multi-label recognition of license plates.

Both U-Net and CNN implementations are based on TensorFlow's Keras library, ensuring a modern and efficient deep learning experience.

Development Environment

This project is set up and tested using the following software environment:

• Python: 3.6
• TensorFlow: 1.15.2
• OpenCV: 4.1.0.25
• Keras: 2.3.1

How It Works

The project follows a streamlined process to ensure accurate license plate recognition:

1. Image Segmentation: U-Net performs image segmentation to create a binary image that isolates potential license plate areas.
2. Edge Detection and Correction: OpenCV detects the edges of the isolated areas to determine the position of the license plate. It then corrects any perspective distortions to ensure the plate is in an ideal position for recognition.
3. License Plate Recognition: The processed image is fed into a CNN, which performs end-to-end multi-label recognition to accurately read the license plate characters.

For a detailed explanation of this process, refer to the project’s documentation on the accompanying CSDN blog: CSDN Blog.

Recognition Capabilities

The software excels in situations where traditional systems might fail:

• It performs well even when images are taken from awkward angles or in conditions with high exposure or poor lighting.
• The software boasts superior recognition capabilities, sometimes exceeding other prominent AI-based plate recognition systems.

Usage Considerations

• When working with images of complete license plates that do not require additional localization, ensure the image dimensions are 240 x 80 pixels or smaller. Larger images could be misinterpreted, leading to unnecessary localization attempts and reduced accuracy.

Demonstration

The project includes several example images showcasing its capabilities. These images demonstrate the software's effectiveness across various scenarios, illustrating its robustness and adaptability.

Additional example images can be seen in the project repository, showcasing the accuracy and reliability of this system across different conditions. Each example highlights the potential of this technology to transform license plate recognition tasks into more streamlined and accurate processes.