License Plate Recognition with Compressive Sensing Based Feature Extraction

TL;DR

This paper introduces a license plate recognition system that employs compressive sensing for efficient feature extraction, enabling accurate vehicle identification with reduced hardware complexity and computational requirements.

Contribution

It presents a novel approach combining compressive sensing with SVM for license plate recognition, improving efficiency and reducing hardware costs.

Findings

Effective feature extraction with compressive sensing

Reduced training data requirements

Lower computational complexity

Abstract

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.