License Plate Segmentation for Intelligent Transportation Systems

Cinsdikici, Muhammed; Tunali, Turhan

doi:10.1007/978-3-540-39737-3_55

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…Sobel is simple and suitable for vertical edge enhancement. 20 After applying vertical Sobel filter on image, we get the vertical dense information. It clearly carries plate region as interest area.…”

Section: Segmentation: Finding Platementioning

confidence: 99%

“…We search the plate with density function bounded by frame having 1/6 ratio. 20 In this search, candidate plate regions are scored as in Fig. 3(b).…”

Section: Segmentation: Finding Platementioning

confidence: 99%

“…BVT defines the candidate cuts with scoring confirmation. 20 Projected signal seen in Fig. 6 is linearly transformed along 'y' axis.…”

Section: Segmentation: Isolating Charactersmentioning

confidence: 99%

See 2 more Smart Citations

Automatic number plate information extraction and recognition for intelligent transportation system

Cinsdikici¹,

Uğur²,

Tunali³

2007

The Imaging Science Journal

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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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Section: Segmentation: Finding Platementioning

confidence: 99%

“…We search the plate with density function bounded by frame having 1/6 ratio. 20 In this search, candidate plate regions are scored as in Fig. 3(b).…”

Section: Segmentation: Finding Platementioning

confidence: 99%