Automatic number plate information extraction and recognition for intelligent transportation system

Abstract

In this paper, a new license plate information retrieval system is designed and developed. The system has two main modules: segmentation and recognition. In segmentation, interested information on the image is extracted through the processes of Kaiser resizing, morphological filtering, artificial shifting and bi-directional vertical thresholding. In recognition module, a novel approach for principal component analysis (PCA) and fast backpropagation neural net composition is used as a recognizer. The novel approach is about the construction of Eigen space through the PCA that is used for feature extraction. Our approach is more tolerable to the problems of classical application PCA such as rotation, scaling and character width dependence. The outputs of the new feature extractor used as inputs to the fastbackpropagation neural net recognizer module. This neural network trained with scaled conjugate gradient function. For each module, alternative available methods are mentioned and proper sequence of operations is developed. Finally, overall performance of the system is exported.

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“…The introduced method worked effectively under light variations and even under shadow. Cinsdikici et al [15] suggested a new information retrieval system of number plate recognition. The suggested method was a combination of two major steps namely, character segmentation and number recognition.…”

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“…Thirdly the histogram of brightness was shifted by | − |. Consequently, the drift coefficients and were calculated, using (9), where the two regions were separated by the brightness baseline.…”

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