Low complexity offline character recognition of license plate using perspective view

License plate detection and character recognition have unveiled new possibilities and challenges in the field of intelligent transport system. Numerous algorithms have been proposed regarding license plate localization and tracking, character segmentation and character recognition. License plate character recognition is still an active area of research specially in terms of processing complexity. A handful of feature extraction algorithms including vertical and horizontal histogram, center of gravity, structural characteristics, modified edge maps, image projection, multi zoning, adaptive multi zoning, concavities measurement, gradient directional features, median gradient features, longest-run features and momentum features have been proposed. Neural network followed by ensemble classifier shows recognition rate in the high nineties. However, ensemble classifier based on multi-feature is burdened with computational intricacy and lethargic processing. This paper presents a perspective view based license plate character recognition algorithm that copes with some of the inherent problems regarding character recognition. The proposed algorithm is resilient against slant, skewness and obscurity of image while limiting processing time and memory usage. Our algorithm tracks the principal curvature of the segmented image in terms of magnitude and inter-impulse distance. A simple back propagation neural network offers recognition rate of 93% with faster training and validation time. A thorough analysis of the algorithm and meticulous details of implementation are presented throughout this paper.