Application-Oriented Farsi License Plate Recognition Using Deterministic Clustering Algorithm and Mser Detector

The main contribution of this paper is proposing a novel deterministic and non-iterative clustering algorithm for automatic farsi license plate recognition (alpr). in fact, after discarding some regions with low probability in terms of being license plate, the edge points inside every separately remained region are considered as a cluster and a gaussian component is estimated using expectation-maximization (e-m) algorithm, for every cluster. candidate regions are obtained by applying application-oriented thresholds for size, aspect ratio and orientation to gaussian components. then the license plate regions are identified by counting regions like to numeric characters, exploiting maximally stable extremal regions (mser) detector whereas numeric characters are extracted by using a proposed algorithm, for discarding non-character regions. so, the algorithm is able to segment the license plate numeric and alphabetic characters simultaneously. finally for character recognition, a new simple, fast and robust algorithm, which uses feature extraction and template matching technique, is proposed. the method is evaluated for detection and recognition, by using an iranian image database that includes access control (ac), law enforcement (le) and road patrol (rp) applications. the method is robust to rotation, skew, and multiplicity of license plate and low quality and complex background images.