Automatic Number Plate Recognition Project
- Naveen
- 0
In recent years, automatic license plate recognition or license plate recognition has become one of the useful approaches for vehicle surveillance. This article presents an automatic license plate recognition project using OpenCV and EasyOCR.
Traffic control and vehicle owner identification have become major problems in all countries. It can be difficult to identify the owner of a speeding vehicle that violates the road rules.
Therefore, due to the speed of the vehicle, traffic personnel may not be able to obtain the vehicle number of the moving vehicle, so such people cannot be caught and punished. To solve such problems, we need to develop a license plate recognition system. There are many license plate recognition systems available today.
These systems are based on a variety of methods, but factors such as high vehicle speed, inconsistent vehicle license plates, vehicle number language, and varying lighting conditions have a large impact on the overall recognition rate. It’s always been a very difficult task because of the impact. Most systems work under these constraints. The next section presents an automatic number license plate recognition using OpenCV and EasyOCR.
To start this task, we will import necessary libraries.
Let’s read an image and convert that into grayscale and display that image.
Let’s apply filter to remove noise from our image and get the edges of the image and display the output.
Let’s find out the contours and apply mask on our image and display the output.
Now we will use the EasyOCR to read the text
Let’s check the result on our image.
I hope you enjoyed this article on automatic license plate recognition using OpenCV and EasyOCR.
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Author
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Naveen Pandey has more than 2 years of experience in data science and machine learning. He is an experienced Machine Learning Engineer with a strong background in data analysis, natural language processing, and machine learning. Holding a Bachelor of Science in Information Technology from Sikkim Manipal University, he excels in leveraging cutting-edge technologies such as Large Language Models (LLMs), TensorFlow, PyTorch, and Hugging Face to develop innovative solutions.
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