Automatic license plate detection and recognition framework to enhance security applications
| Autor: | Myung-Ryul Choi, Khurram Khan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Boosting (machine learning)
business.industry Computer science Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Feature selection 02 engineering and technology Optical character recognition Image segmentation computer.software_genre Linear discriminant analysis Convolutional neural network Atomic and Molecular Physics and Optics Computer Science Applications 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Artificial intelligence Electrical and Electronic Engineering business computer |
| Zdroj: | Journal of Electronic Imaging. 28:1 |
| ISSN: | 1017-9909 |
| DOI: | 10.1117/1.jei.28.1.013036 |
| Popis: | We develop an automatic license plate recognition (ALPR) system for enhancing the investigation capabilities of law enforcing agencies to monitor suspicious vehicles. The recognition performance of real-time ALPR systems is affected to great extent in challenging conditions such as varying illumination, angle-of-view, different sizes of plates, changing contrast, and shadows. Moreover, character segmentation step sensitivity to plate resolution, size of characters, occluded characters, and width between characters makes it difficult to properly isolate the character, which further degrades recognition accuracy. In the first step of the proposed framework, a plate is localized using the faster region-based convolutional neural network method. In the second step, our study proposes a segmentation-free plate recognition approach that applies an adaptive boosting method with linear discriminant analysis for feature selection followed by matching the plates with a database for suspected vehicles and information retrieval. Simulation results show that the proposed framework is more robust to illumination variations, low-resolution images, different orientations, and variable license plate sizes than the conventional ones. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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