Improving the License Plate Character Segmentation Using Naïve Bayesian Network

Abstract

: Character segmentation plays a pivotal role in automatic license plate recognition (ALPR) systems. Assuming that plate localization has been accurately performed in a preceding stage, this paper mainly introduces a character segmentation algorithm based on combining standard segmentation techniques with prior knowledge about the plate’s structure. We propose employing a set of relevant features on-demand to classify detected blocks into either character or noise and to refine the segmentation when necessary. We suggest using the na¨ıve Bayesian network (NBN) classifier for efficient combination of selected features. Incrementally, one after one, high computational cost features are computed and involved only if the low-cost ones cannot decisively determine the class of a block. Experimental results on a sample of Algerian car license plates demonstrate the efficiency of the proposed algorithm. It is designed to be more generic and easily extendable to integrate other features into the process.