In this paper, an approach to vehicle license plate localization is described. The algorithm starts by isolating objects, in the image, that can be possible candidates of characters in a license plate. It then uses distances between objects and their relative positions to identify possible groupings (series) of characters that could belong to a license plate. The algorithm then uses a novel character recognition approach to recognize the identity of each character in the series. Finally, the relative positions of series are used to localize the license plate. The algorithm was successfully applied to images containing Lebanese License plates.
This paper describes an approach to character recognition which is designed to recognize Lebanese license plate characters by extracting lines of information from vehicle photographs. The algorithm is designed to extract signatures at strategic positions on the character, then studies the information taken from the hotspots and identifies the different characters. The algorithm is simple and fast however its simplicity does not compromise the integrity of license plate recognition systems.
In this chapter, localizing Saudi license plates in images and recognizing characters automatically in those plates are described. Three algorithms to recognize English and Arabic characters in Saudi license plates are presented. The three algorithms rely on processing information from lines strategically drawn vertically and horizontally through a character. In most of the cases, all letters and numbers were able to be recognized. Furthermore, two approaches for localization, “object adjacency” and “character recognition,” are described in this chapter. The algorithms were successfully applied to images containing Saudi License plates as shown through the results presented. A hybrid approach is also presented in which vertical alignment was used to aid the recognition phase in correctly recognizing characters. The hybrid method is only applicable to new Saudi license plates since they contain redundant information in both Arabic and English sections.
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