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License plate detection and recognition, also known as Automatic Number Plate Recognition (ANPR) or Automatic Vehicle Identification, is a surveillance method that is required for a number of purposes including law enforcement, parking lot allocation, gate entry control, etc. Performing this task without using large, bulky and expensive sensors/hardware is a challenging issue. Relevant literature in this context suggests the use of image processing. Due to the efficacy of image processing, a number of ANPR solutions have been introduced. However, these solutions are either limited in…mehr

Produktbeschreibung
License plate detection and recognition, also known as Automatic Number Plate Recognition (ANPR) or Automatic Vehicle Identification, is a surveillance method that is required for a number of purposes including law enforcement, parking lot allocation, gate entry control, etc. Performing this task without using large, bulky and expensive sensors/hardware is a challenging issue. Relevant literature in this context suggests the use of image processing. Due to the efficacy of image processing, a number of ANPR solutions have been introduced. However, these solutions are either limited in operations or work only under specific conditions and environments. Additionally, these systems have certain limitations which make these unfeasible for the implementation. In order to address the issues pertaining to the existing solutions for ANPR, we propose a robust solution for ANPR in this book.
Autorenporträt
Zuhaib Ahmed Shaikh obtained his Bachelors and Masters degrees in computer systems engineering from Quaid-e-Awam University Nawabshah-Pakistan. Currently, he is working as lecturer in computer systems engineering department in the same university. His research interests include image processing, machine learning and embedded systems.