Andere Kunden interessierten sich auch für
![HAND GESTURE DETECTION AND RECOGNITION BUILDING SYSTEM]( "HAND GESTURE DETECTION AND RECOGNITION BUILDING SYSTEM")
HAND GESTURE DETECTION AND RECOGNITION BUILDING SYSTEM29,99 €![Detection of Fast Flux Network Based Social Bot Using AB Techniques]( "Detection of Fast Flux Network Based Social Bot Using AB Techniques")
Detection of Fast Flux Network Based Social Bot Using AB Techniques23,99 €![Pose Invariant Face Recognition using PCA]( "Pose Invariant Face Recognition using PCA")
Pose Invariant Face Recognition using PCA36,99 €![Arrhythmia Detection by using Generative Adversarial Network Method]( "Arrhythmia Detection by using Generative Adversarial Network Method")
Arrhythmia Detection by using Generative Adversarial Network Method51,99 €![Face Detection using Proposed Model]( "Face Detection using Proposed Model")
Face Detection using Proposed Model23,99 €![A Hybrid DWT, PCA and ICA Features for Face Recognition using ANN]( "A Hybrid DWT, PCA and ICA Features for Face Recognition using ANN")
A Hybrid DWT, PCA and ICA Features for Face Recognition using ANN23,99 €![FAKE REPORT DETECTION USING MACHINE LEARNING]( "FAKE REPORT DETECTION USING MACHINE LEARNING")
FAKE REPORT DETECTION USING MACHINE LEARNING29,99 €
An integrated vehicle plate detection and recognition system generally aims to detect the license plate (LP) and recognize its characters. The process basically includes LP detection, LP extraction, character segmentation, feature extraction and recognition. Due to its wide range of applications such as traffic management, security etc., and this topic is intensively researched especially in the field of image processing. Furthermore, the differences in systems, colours, backgrounds, foregrounds, font and style of the license plates from one country to another add more problems and challenges for new researches. Thus, this book presents an integrated approach for detecting and recognizing Libyan license plates based on Radial Basis Function Neural Network (RBFNN). The method begins with the preprocessing of the image using edge detection and morphological operations. In the detection stage, connected component analysis is used to locate unique objects. In the recognition process,for character segmentation, a simple template is derived to extract and differentiate digits and Arabic words. Finally,in the classification process, RBFNN is used to recognize an individual character.