Preprocessing and Segmenting License Plates Using Top-Hat and Bottom-Hat Transforms, Thresholding, and Binarization Methods

When processing license plate images, we employ several enhancement techniques including top-hat transformation, bottom-hat transformation, threshold selection, and binarization to improve image quality and achieve segmentation results. The top-hat transformation highlights bright regions of the license plate (typically letters and numbers), while the bottom-hat transformation emphasizes darker areas (usually the plate background). Through threshold selection algorithms (such as Otsu's method or adaptive thresholding) and subsequent binarization, we convert the grayscale license plate image into a binary format with only black and white pixels. This conversion facilitates more efficient subsequent processing operations. The combined application of these preprocessing methods significantly enhances license plate image quality and enables accurate segmentation for character recognition systems. Implementation typically involves using image processing libraries like OpenCV with functions such as cv2.morphologyEx() for morphological operations and cv2.threshold() for binarization processing.