Abstract
In this paper, we present a new design flow for robust license plate localization and recognition. The algorithm consists of three stages: 1) license plate localization; 2) character segmentation; and 3) feature extraction and character recognition. The algorithm uses Mexican hat operator for edge detection and Euler number of a binary image for identifying the license plate region. A pre-processing step using median filter and contrast enhancement is employed to improve the character segmentation performance in case of low resolution and blur images. A unique feature vector comprised of region properties, projection data and reflection symmetry coefficient has been proposed. Back propagation artificial neural network classifier has been used to train and test the neural network based on the extracted feature. A thorough testing of algorithm is performed on a database with varying test cases in terms of illumination and different plate conditions. Practical considerations like existence of another text block in an image, presence of dirt or shadow on or near license plate region, license plate with rows of characters and sensitivity to license plate dimensions have been addressed. The results are encouraging with success rate of 98.10% for license plate localization and 97.05% for character recognition.
Highlights
License plate recognition is considered to be one of the fastest growing technologies in the field of surveillance and control
We present a new design flow for robust license plate localization and recognition
The algorithm consists of three stages: 1) license plate localization; 2) character segmentation; and 3) feature extraction and character recognition
Summary
License plate recognition is considered to be one of the fastest growing technologies in the field of surveillance and control. In order to make the texture information of license plate clearer, a technique based on obtaining horizontal image difference was presented in [3]. This method is sensitive to the license plate dimensions and is not robust enough to handle all practical conditions. A method based on the concept of stroke direction and elastic mesh [8] was proposed with an accuracy in the range of 97.8 99.5% Another important aspect of character recognition step is the type of classifier employed. The images are taken from a commercial City Sync’s automatic number plate recognition camera video [14]
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