This issue introduces the identification of an image’s rotating verification code. The idea of recognition is actually similar to that of the previous article’s recognition of the sliding verification code, which is also borrowed from it. However, the rotating verification code is more complicated and difficult to implement. Let’s take a look at the original data dataset after collection and recognition.
Original data set:
the data set after the circle graph is successfully rotated:
Note: I only grabbed dozens of pictures as the data set, but the effect is already obvious, and there is no need for a large number of data sets to use the deep learning model for training, here It does not involve deep learning or machine learning. It just uses simple image processing knowledge. If you have higher requirements for accuracy, you can try to modify some parts of the code that control the threshold. The threshold will eventually affect the accuracy.
