Building Computer Vision Projects with OpenCV 4 and C++

Extremal region filtering

Although MSERs are a common approach to define which extremal regions are worth working with, the Neumann and Matas algorithm uses a different approach, by submitting all extremal regions to a sequential classifier that's been trained for character detection. This classifier works in two different stages:

1. The first stage incrementally computes descriptors (bounding box, perimeter, area, and Euler number) for each region. These descriptors are submitted to a classifier that estimates how probable the region is to be a character in the alphabet. Then, only the regions of high probability are selected for stage 2.
2. In this stage, the features of the whole area ratio, convex hull ratio, and the number of outer boundary ...