License Plate Recognition Using YOLOv4 Object Detection, OpenCV, and Tesseract OCR

Learn how to implement your very own license plate recognition using a custom YOLOv4 Object Detector, OpenCV, and Tesseract OCR! In this tutorial I will walk-through custom code I have created to run object detections to find license plates, crop the license plate region, preprocess the license plate using OpenCV, and then run it through Tesseract OCR to output the license plate number. The backbone of this code is TensorFlow and it is written in Python.

#objectdetection #OCR #licenseplaterecognition

This video covers the implementation of a custom YOLOv4 model to detect license plates and numerous preprocessing techniques such as blurring, thresholding, dilation, finding contours, and character segmentation. All of these are done in order to properly prepare the license plate in order for Tesseract OCR to extract the license plate text.

Video Breakdown:
1. Cloning or Downloading the Code for the Tutorial
2. Installing Dependencies and saving the custom YOLOv4 model
3. Explanation of the license plate recognition and code.
4. Running the License Plate Recognition on several images.

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Thanks so much for watching!
- The AI Guy

[TheAIGuy]

Hope you enjoy this video! Let me know which field of AI or Computer Vision you want me to make videos on next? :)

[r]

Awesome tutorial as always!! Amazing!!

[marypaul]

Just subscribed before following the video this is what have been looking for!

[marypaul]

Wow, your videos deserve recognition..trying it out

[raymondmichael]

I spend several minutes reading comments, to my surprise, you don't only pay much attention on creating good content but also you do well to answer comments. I hate those who just dump tutorials and don't even bother to attend to viewers.
Keep the good work.

Greetings from Tanzania 🇹🇿

[Claudio-gvlz]

thanks so much man, im doing my final year of university and im doing something similar for my final project, im still a bit confused by this because i never really used python but it was very helpful

[shis]

Very helpful video . Thanks 😃
You deserve 10M Subscriber

[akshayjaryal]

Thanks @AIGUY for delivering great content.

[hammadahmed]

Thank you so much you really help me allot in my project stay happy.

[NilRipamonti]

Thanks for the video. 9:26 It might have no meaning for your example, but some places in the world those small icons, hifens or spaces changes completely the meaning of a license plate.

[levimenezes]

Congratulations on the work, this video is helping me a lot, you could show how to use these same techniques on android

[pengjiabeitang]

omg! thanks a lot! this really save me

[arthurfarrapo]

Thx, i made a market bot to a game using this!

[tusharjain]

Thanks Guy for the videos. These are easy to follow and works perfect.
Just want to know if we can add new my own class to existing yolov4 classes. So that I don't need to retrain whole model from scratch?

[damienp]

Great job and explanation. I learnt a lot with this tutorial and others (deepsort one). Deepsort works nice on cars with yolov4-tiny. I'm wondering if it would be possible to train this custom plate detector for yolov4-tiny and make it faster?

[avielalexander]

Amazing, Thanks for this one!
When using a YoloV3 weights file, does it dramatically reduce the speed of detection? And also, on a standard PC with the built-in intel graphics GPU, should I opt for the CPU installation?

[sagartiwari]

Hi mate, thanks for the amazing video. How can I save the predicted coordinates in a "Result.txt" file, like we are doing earlier ?

[ridhazaghdoud]

thanks for your job, can you explain this:
download the binary files and set them up on your local machine

[Glopok]

First of all, thx for ur work.
Because of you i started with python. And now i try to learn more about DL and object detection.

I wanna test out how much slower the object detection on my GPU is in comparision to the GPU from Google Colab.
In your Colab tutorials u implemented a time measurment for the object detection.

Can u give me a advice how i can implement such a thin in this --info function?

[shubhamshah]

Hello, I've been following your tutorials, they're really awesome. I've been working on this project since 2 weeks and I've tried several settings of Tesseract but it isn't working as expected. Because of skew and other problems, I haven't tried your code yet. But anyway, keep making such good content.