Optimized Two-Stage Ensemble Model for License Plate Recognition

Pattern recognition models play an important role in many real-world applications such as text detection and object recognition. Numerous methodologies including Computational Intelligence (CI) models have been developed in the literature to tackle image-based pattern recognition problems. Focused on CI models this research presents efficient Particle Swarm Optimization (PSO)-based models and their application to license plate recognition. Firstly a new Reinforcement Learning-based Memetic Particle Swarm Optimization (RLMPSO) model is introduced. Then RLMPSO is integrated with the Fuzzy Support Vector Machine (FSVM) to formulate an efficient two-stage RLMPSO-FSVM model. Specifically two-stage RLMPSO-FSVM comprises an ensemble of linear FSVM classifiers that are constructed using RLMPSO to perform parameter tuning feature selection as well as training sample selection. Finally the proposed two-stage RLMPSO-FSVM model is applied to a real-world Malaysian vehicle license plate recognition (VLPR) task.