Vol. 1 Issue 3
Year: 2014
Issue:Sep-Nov
Title:Survey on Various Types of License Plate Localization, Segmentation & Recognition Algorithms
Author Name:C.Anantha Reddy and C.Shoba Bindu
Synopsis:
Automatic License Plate Recognition (ALPR) is a challenging area of review due to its importance to a wide variety of its commercial applications. In any ALPR, there are three phases, which are used for license plate recognition. The initial phase is to capture the car image using sensors like camera and extract the license plate image from the input image. The next phase is to segment the license plate for extracting the characters from the image of the license plate which are based on features like color, shape, etc. The final phase is to detect and recognize the segmented characters of the license plate. This paper reviews different types of approaches and its challenges involved in localization, Segmentation and recognition of license plate numbers. Extensive studies are also made and the recognition accuracies are compared. This paper also suggests the best possible combination within all the proposed techniques to achieve higher accuracies with minimum resources.
Year: 2014
Issue:Sep-Nov
Title:Survey on Various Types of License Plate Localization, Segmentation & Recognition Algorithms
Author Name:C.Anantha Reddy and C.Shoba Bindu
Synopsis:
Automatic License Plate Recognition (ALPR) is a challenging area of review due to its importance to a wide variety of its commercial applications. In any ALPR, there are three phases, which are used for license plate recognition. The initial phase is to capture the car image using sensors like camera and extract the license plate image from the input image. The next phase is to segment the license plate for extracting the characters from the image of the license plate which are based on features like color, shape, etc. The final phase is to detect and recognize the segmented characters of the license plate. This paper reviews different types of approaches and its challenges involved in localization, Segmentation and recognition of license plate numbers. Extensive studies are also made and the recognition accuracies are compared. This paper also suggests the best possible combination within all the proposed techniques to achieve higher accuracies with minimum resources.
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