Cascade-Based License Plate Localization with Line Segment Features and Haar-Like Features
AdaBoost classifiers with Haar-like features are widely used for license plate (LP) localization. However, it normally requires high-dimensional Haar-like features which cause extremely high computational cost. In this paper, a rejection cascade was built for LP localization with reduced Haar-like features. We first introduced line segment features as pre-input of Haar-like features for AdaBoost to eliminate more than 70% of the background in an image. Line segment features, including density, directionality and regularity, were extracted from line segments, which were detected by applying Hough Transform on an edge image. Later, AdaBoost classifiers with Haar-like features were further applied to identify the exact location of license plates. Our method dramatically reduced the demanded dimensions of Haar-like features, therefore saved much time in AdaBoost training stage. By comparing our method with methods of only using Haar-like features and only using line segment features, experimental results demonstrated that our proposed method achieved the best detection rate with significantly reduced dimensions of Haar-like features.