Optimizing Statistical Character Recognition Using Evolutionary Strategies to Recognize Aircraft Tail Numbers

The design of statistical classification systems for optical character recognition (OCR) is a cumbersome task. This paper proposes a method using evolutionary strategies (ES) to evolve and upgrade the set of parameters in an OCR system. This OCR is applied to identify the tail number of aircrafts moving on the airport. The proposed approach is discussed and some results are obtained using a benchmark data set. This research demonstrates the successful application of ES to a difficult, noisy, and real-world problem.