Details and Errors

• I used the object detection model because it's way more accurate and way more resilient than using traditional computer vision techniques.
• Verification text ran for 21 tests with approximately 17.079 secs.
• Here label map represents all the possible objects that the model will detect which is one object that is the license plate.
• Now, before starting with OCR, one should pip install Pytorch with CUDA acceleration, now it all depends on a particular's machine so a user can install **Pytorch with GPU (CUDA 11.3) ** conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch or without GPU conda install pytorch torchvision torchaudio cpuonly -c pytorch, this all is for windows machine.
• It generally be wise to split the GPU because generally TensorFlow training model uses the whole GPU and then it slows our detection process.
• I used EasyOCR because it is better on numbers and has an accuracy of around 95%.
• For detecting license plates, generally the threshold is set to 0.8 for high accuracy.
• By default OpenCV works with the colour format [BGR] so one should use cv2.COLOR_BGR2RGB.
• To save images with a unique name in the folder and in the .csv file, I used UUID that allows creating a unique uniform identifier.
• Average Precision (AP) is obtained by finding the area under the precision-recall curve. The mAP for object detection is the average of the AP calculated for all the classes to determine the accuracy of a set of object detections from a model when compared to ground-truth object annotations of a dataset.
• SSD MobileNet V2 FPNLite 320x320: speed - 22ms and COCO mAP - 22.2, got these information from tensorflow zoo model

Errors I faced.

• Faced an error while importing wget library.
• Faced an error while installing pycocotools. Error: Value Error: numpy.ndarray size changed may indicate binary compatibility. Expected 88 from encoder, got from pyobject.
• While installing EasyOCR, my machine didn't support PyTorch with CUDA 11.3, so due to that I faced an error.