I just posted V2.0 in the first post. I still need to write up the instructions on how to set it up. I am hoping to finish the instructions sometime today.
I took a break from my DeepStack Custom Model project and started work on a DeepStack LPR solution.
I made a lot of progress on the DeepStack LPR. The solution is made up of one Python scripts that crops and rotates (if needed) the license plate. Then the script reads all of the characters in the plate and logs the license plate details. Also the script will save the DeepStack results in JSON format and YOLO format. This data can be used to improve DeepStack Custom Model that does the OCR.
The model that does the OCR needs some work I would say it is about 75% accurate. The way the script is written it will save a small text file that can be used to retrain the OCR model. Once everyone starts using the DeepStack LPR models and scripts for a week or two you can send me the alert images and the text files with these files I will retrian the OCR model. If we do this about two to three times we should have a accurate OCR model. I will post more details later.
Also your antivirus might detect the scripts as a virus because the antivirus software has no signature reference for the Python scripts since they are not mass produced software. My Norton Antivirus did detecte them as a virus and I had to let Norton Antivirus know it is OK to run.
Version 2.0 notes
- Only one script is needed for V2.0. The script does the cropping and rotation (if needed). Once cropped the script will have DeepStack run the OCR on the cropped plate.
- After the OCR the script will save the DeepStack results in JSON format and YOLO format (for future retraining of the OCR custom model)
- Since there is only one script only one camera is needed with the cropped license plate.
- Also the new version is twice as fast as V1.1 since there is only one script.
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