Now every company is trying to provide its users with the facility to perform their task without the need for any human intervention.In this talk, we are addressing a similar problem of automating the vehicle parking systems.
This problem has been targeted with a variety of algorithms like traditional template matching to advance deep learning algorithms like YOLO. Beginners knowledge of the following items would be helpful. This much is enough, we would also be covering the important content in the talk. The motive should be to understand the basic working of Convolutional Neural Networks. The coding part will be explained in the talk as well relating each step to its working. They had been working in this field for the past one-half year and have worked on a variety of projects ranging from customer segmentation, recommendations to the projects from the field of computer vision, deep learning and reinforcement learning. ![]() Python Plate Recognition License Plate FromWe can extract the license plate from an image using some computer vision techniques and then we can use Optical Character Recognition to recognize the license number. Here I will guide you through the whole procedure of this task. For this apply Otsus Thresholding on the vertical edge image. ![]() Closing is useful to fill small black regions between white regions in a thresholded image. It is important to binarize and morph the image before finding contours so that it can find more relevant and less number of contours in the image. If you draw all the extracted contours on original image, it would look like this. We have defined the minimum and maximum area of the plate as 4500 and 30000 respectively. After validating you will get a perfect contour of a license plate. For that first step is to extract the value channel from the HSV format of the plates image. The image of plate can have different lightning conditions in different areas, in that case adaptive thresholding can be more suitable to binarize because it uses different threshold values for different regions based on the brightness of the pixels in the region around it. After extracting the contours take the largest one, find its bounding rectangle and validate side ratios. Python Plate Recognition Full Source CodeSteps 8 to 13 are performed by segmentchars function that you can find below in the full source code. Python Plate Recognition Driver Code ForThe driver code for the functions used in steps 6 to 13 is written in the method checkplate of class PlateFinder. Method findpossibleplates precprocess the image with preprocess method then extracts contours by extractcontours method then it checks side ratios and area of all extracted contours and cleans the image inside the contour with checkplate and cleanplate methods. After cleaning the contour image with cleanplate method, it finds all characters on the plate with findcharactersonplate method. It finds characters by computing the convex hull of the contours of a thresholded value image and drawing it on the characters to reveal them. Code: Make another class to initialize Neural Network to predict the characters on the extracted license plate. Code: Create a main function to perform the whole task in a sequence. Get hold of all the important CS Theory concepts for SDE interviews with the CS Theory Course at a student-friendly price and become industry ready. If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contributegeeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks. Please Improve this article if you find anything incorrect by clicking on the Improve Article button below.
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