In this step, we have to detect all the plates in a current camera frame. To do this task, we divide it in two main steps: segmentation and segment classification. The feature step is not explained because we use the image patch as a vector feature.

In the first step (segmentation), we will apply different filters, morphological operations, contour algorithms, and validations to retrieve parts of the image that could have a plate.

In the second step (classification), we will apply a Support Vector Machine (SVM) classifier to each image patch, our feature. Before creating our main application, we will train with two different classes: plate and non-plate. We will work with parallel frontal view color images having 800 pixels of width and taken between 2 and 4 meters from a car. These requirements are important for correct segmentations. We can get perform detection if we create a multi-scale image algorithm.

In the next image, we will shown all process involved in plate detection...