> The intent of this article is to sensibilize [sic] the reader that a machine learning approach in not always the best or first solution to solve some object d

> The intent of this article is to sensibilize [sic] the reader that a machine learning approach in not always the best or first solution to solve some object detection problems This is a good lesson to teach people! But I don't think that this page does so successfully. By far and away the biggest issue is that they are only demonstrating on a single image. You can get almost any method to work on a single image, especially when you're interactively designing the method to work on that specific image. If you want a method that works in unforeseen conditions, you need to think carefully about your assumptions... * Threshold on 0.5–will that work at night? What if you're driving into the sun and the image is overexposed? * Dilating canny edges and finding connected components–what if the car is white with black trim, and the dilation merges the license plate CC with another one? What if the car is further away, and the dilation factor merges the entire car into one big blob? * Filtering the blobs by ratio–what if the car is at an angle (turning, or starting up a hill), so the ratio is off? What if they have bumper stickers that are the same shape, and fool the filter? While you can make a reasonable license plate detector with traditional methods (see, for example, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.455.2440&rep=rep1&type=pdf http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.455...), the advantage of using ML methods is that, if done correctly, they actually have a chance of working in most the situations you encounter (including the assumption-violating ones above), rather than just the ones you can think of while making the system. That lesson is probably just as important as the "don't jump straight to ML" one.