![]() While there are a few “gotchas” that may trip you up, once you see them first hand, I think you’ll be able to spot them in the future when you work on your own projects. All three of these aspects can cause problems for our character segmentation algorithm, so we’ll need to take special care as we write our code.īut no worries - I’ve got you covered. Sure, if we apply basic thresholding, we’ll be able to extract the license plate characters - but we’ll also extract a bunch of other “stuff” that doesn’t interest us, such as any bolts fastening the license plate to the car, branding logos on the plate itself, or embellishments on the license plate frames. On the surface, this step looks quite easy - all we need to do is perform basic thresholding, and we’ll be able to extract the license plate characters, right? These license plate regions are called license plate candidates - it is our job to take these candidate regions and start the task of extracting the foreground license plate characters from the background of the license plate. In our previous lesson, we learned how to localize license plates in images using basic image processing techniques, such as morphological operations and contours.
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