Construction of efficient detectors for character information recognition
This work addresses object recognition in video for applications like surveillance or transportation, but it is incremental as it primarily combines and modifies well-known methods without introducing a fundamentally new paradigm.
The paper tackles the problem of recognizing objects like faces, vehicles, and characters in video images by developing a universal approach that combines existing methods, resulting in the construction of 11 types of detectors for tasks such as counting railway carriages and recognizing digits from zero to nine.
We have developed and tested in numerical experiments a universal approach to searching objects of a given type in captured video images (for example, people's faces, vehicles, special characters, numbers and letters, etc.). The novelty and versatility of this approach consists in a unique combination of the well-known methods ranging from creating detectors to making decisions independent of the type of recognition objects. The efficiencies of various types of basic features used for image coding, including the Haar features, the LBP features, and the modified Census transformation are compared. A combination of the modified methods is used for constructing 11 types of detectors of the number of railway carriages and for recognizing digits from zero to nine. The efficiency of the constructed detectors is studied.