Want to be able to perform number plate recognition in real time?

Well in this course you'll learn how to do exactly that!

In this video, you'll learn how to leverage Python Tensorflow Object Detection to be able to detect license plates using Kaggle Data. Once those plates have been detected you'll then be able to apply OCR to extract the text from each and every plate using PyTorch and EachOCR.

On top of it all, you'll be able to export your results. We'll setup a saving function to output the regions of interest as well as the detected text. This could be used as part of a broader system or standalone.

In this video you’ll learn how to:
1. Detect license plates from images and in real time from video
2. Apply a EasyOCR to license plates to extract the plate number
3. Save license plates detected for future analysis and searching

Links
Final Notebook: [ Ссылка ]
Baseline Code: [ Ссылка ]
PyTorch: [ Ссылка ]
Full Tutorial: [ Ссылка ]

Chapters:
0:00 - Start
0:26 - Tutorial Start
1:11 - Gameplan
3:58 - PART 1 | Setup
7:13 - Cloning Baseline Code
10:42 - Creating a Virtual Environment
12:51 - Installing Dependencies
17:55 - Installing Tensorflow Object Detection
32:47 - Cloning Pre-Trained Models
34:21 - PART 2 | Data
34:44 - Cloning Images from Kaggle
35:52 - Creating a Training and Testing Partition
41:33 - PART 3 | Training
42:59 - Updating the LabelMap
44:37 - Creating TF Records
49:30 - Updating Transfer Learning Config
51:51 - Training the Model
1:00:00 - PART 4 | Detecting Plates
1:01:00 - Detecting Plates from an Image
1:03:45 - Detecting Plates from Video
1:05:01 - PART 5 | Applying OCR
1:06:29 - Splitting GPU
1:10:46 - Setup EasyOCR
1:13:46 - Applying Detection Thresholding
1:18:42 - Extract Image Width and Height
1:20:16 - Loop Through Detections and Apply OCR
1:25:40 - Filtering Algorithm
1:36:28 - Final OCR Function
1:42:15 - Applying ANPR in Real Time
1:45:38 - PART 6 | Saving Results
1:46:10 - Importing Dependencies
1:46:29 - Building a Save Function
1:52:06 - Saving Plates from. Video

Oh, and don't forget to connect with me!
LinkedIn: [ Ссылка ]
Facebook: [ Ссылка ]
GitHub: [ Ссылка ]
Patreon: [ Ссылка ]
Join the Discussion on Discord: [ Ссылка ]

Happy coding!
Nick

P.s. Let me know how you go and drop a comment if you need a hand!

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