License Plate Recognition with Compressive Sensing Based Feature Extraction
This work addresses the need for cost-effective and robust automatic vehicle identification in traffic control systems, though it appears incremental by applying compressive sensing to a specific domain.
The paper tackled license plate recognition under challenging conditions by proposing a compressive sensing-based feature extraction method, achieving efficient classification with reduced training data and computational demands using a Support Vector Machine.
License plate recognition is the key component to many automatic traffic control systems. It enables the automatic identification of vehicles in many applications. Such systems must be able to identify vehicles from images taken in various conditions including low light, rain, snow, etc. In order to reduce the complexity and cost of the hardware required for such devices, the algorithm should be as efficient as possible. This paper proposes a license plate recognition system which uses a new approach based on compressive sensing techniques for dimensionality reduction and feature extraction. Dimensionality reduction will enable precise classification with less training data while demanding less computational power. Based on the extracted features, character recognition and classification is done by a Support Vector Machine classifier.