Digital Extraction and Recognition of Vehicle License Plates

In this project, we implement digital extraction and recognition of vehicle license plates to facilitate automated processing. To achieve this objective, we employ image processing techniques involving algorithms for image restoration, enhancement, and contrast optimization - typically implemented using OpenCV functions like cv2.equalizeHist() for histogram equalization and cv2.filter2D() for sharpening filters. The workflow includes preprocessing steps such as Gaussian blur (cv2.GaussianBlur()) for noise reduction and adaptive thresholding (cv2.adaptiveThreshold()) for binarization. Furthermore, we integrate machine learning approaches using convolutional neural networks (CNNs) with TensorFlow or PyTorch frameworks to improve recognition accuracy. The CNN architecture may include layers like Conv2D for feature extraction and MaxPooling2D for dimensionality reduction, trained on annotated license plate datasets. Through these systematic implementations, we ensure efficient digital extraction and high-accuracy processing of vehicle license plates.