These parameters are just height and width ratios (i.e. In order to filter out the unwanted regions we apply a couple parameters to be met in order to accept a contour. Next we use opencv to find all the rectangular shaped contours on the image and sort them left to right.Īs you can see this causes many contours to be found other than just the contours of each character within the license plate number. The image is then dilated using opencv in order to make contours more visible and be picked up in future step.
This white text on black background helps to find contours of image. The image is thresholded to white text with black background and has Otsu's method also applied. Since this image is super small the majority of the time we use cv2.resize() to blow the image up 3x its original size.Ĭonvert the image to grayscale and apply a small Gaussian blur to smooth it out. Taking the bounding box coordinates from YOLOv4 and simply taking the subimage region within the bounds of the box. This demo will be showing the step-by-step workflow on the following original image. This section will highlight the steps I took in order to implement the License Plate Recognition with YOLOv4 and potential areas to be worked on further. checkpoints/custom-416 -size 416 -model yolov4 -video 0 -output. checkpoints/custom-416 -size 416 -model yolov4 -video. checkpoints/custom-416 -size 416 -model yolov4 -images.
checkpoints/custom-416 -input_size 416 -model yolov4
