The licenseplate_yolov8_filtersvm_paddleocr from ablanco1950

Project that uses Yolov8 as license plate detector, followed by a filter that is got selecting from a filters collection with a code assigned to each filter and predicting what filter with an SVM process

Download all the files of the project to a folder, unzip the zip files.

All the modules necessary for its execution can be installed, if the programs give a module not found error, by means of a simple pip.

The most important:

paddleocr must be installed (https://pypi.org/project/paddleocr/)

pip install paddleocr

yolo must be installed, if not, follow the instructions indicated in: https://learnopencv.com/ultralytics-yolov8/#How-to-Use-YOLOv8?

pip install ultralytics

Are attached, te best.pt file, that allows the license plate detect for yolo ( may be get from scratch following the steps pointed out in https://github.com/ablanco1950/LicensePlate_Yolov8_Filters_PaddleOCR), and FSRCNN that allows the working of the filter with de same name

As a previous step, the X_train and the Y_train that the SVM needs are created, the X_Train is the matrix of each image and the Y_train is made based on the code assigned (from 0 to 10) to the first filter with which paddleocr manages to recognize that license plate of car in the reference project https://github.com/ablanco1950/LicensePlate_Yolov8_Filters_PaddleOCR.

The Crea_Xtrain_Ytrain.py program is attached (its execution is not necessary), whose result after applying it to different forders of image files (the input folder is indicated in instruction 15) of renamed image cars with their registration plate is saved in the Training folder , consisting of the image itself and a .txt file with the name of the car's license plate and containing the filter code assigned to that image. This Training file that is attached in .zip format is necessary to download it, like the Test.zip file and unzip them to run:

TrainCodFilterSVM.py

The result is the file with the weights: model.pickle (because of its size I cannot upload it to github, but it takes a short time to obtain it) necessary to establish the predictions when executing the program:

GetNumberInternationalLicensePlate_Yolov8_SVMFilters_PaddleOCR_V1.py

Any folder may be tested changing instruction 14, the resultas in LicenseResults.txt file

Comparing with the reference project: https://github.com/ablanco1950/LicensePlate_Yolov8_Filters_PaddleOCR you get a lower precision but a considerable reduction in execution time.

The references are identical to those of the project https://github.com/ablanco1950/LicensePlate_Yolov8_Filters_PaddleOCR:

References:

https://pypi.org/project/paddleocr/

https://learnopencv.com/ultralytics-yolov8/#How-to-Use-YOLOv8?

https://public.roboflow.com/object-detection/license-plates-us-eu/3

https://docs.ultralytics.com/python/

https://medium.com/@chanon.krittapholchai/build-object-detection-gui-with-yolov8-and-pysimplegui-76d5f5464d6c

https://medium.com/@alimustoofaa/how-to-load-model-yolov8-onnx-cv2-dnn-3e176cde16e6

https://medium.com/adevinta-tech-blog/text-in-image-2-0-improving-ocr-service-with-paddleocr-61614c886f93

https://machinelearningprojects.net/number-plate-detection-using-yolov7/

https://github.com/ablanco1950/LicensePlate_Yolov8_MaxFilters

https://github.com/mrzaizai2k/VIETNAMESE_LICENSE_PLATE