Automatic license plate detection and recognition framework to enhance security applications

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.