After successful recognition of the vehicle the data can be accessed and used for post processing operations as required. Ī typical ANPR system goes through the general process of image acquisition (input to the system), number plate extraction (NPE), character segmentation (CS) and character recognition (CR) (as output from the system).
ANPR is being increasingly used to examine the free flow of traffic, facilitating the intelligent transportation. Local governments often fail to recognize the present and potential mobility needs of residents and visitors as traffic rises in these areas. People migrate away from rural areas and choose to live in cities mostly.
The rapid urbanization of countries is a great advancement in our modern world. The main reason is that the ANPR system recognizes the registered number plate with no additional transponder requirements, as compared to the Ultra High Frequency-Radio Frequency Identification (UHF-RFID) systems.
Due to lower provisioning costs, ANPR is often a choice in the toll and parking lot businesses. It has become over the years mobile, first being deployed in vehicles, but now more recently with the advent of smart phone technology, many ANPR systems have become handheld too. It is no longer just the camera on the roadside or at the barrier to the car park. ANPR technology is already contributing towards intelligent transportation systems and is eliminating the need of human intervention. The concept of Autonomous Vehicles is offering many possibilities of changing fundamental transportation systems. This research aims to advance the state-of-knowledge in ITS (ANPR) built on CV algorithms by citing relevant prior work, analyzing and presenting a survey of extraction, segmentation and recognition techniques whilst providing guidelines on future trends in this area.Īutomatic Number Plate Recognition has become part of our lives and promises to stay in future, integrable with proposed transportation technologies. Deep-Learning techniques are widely utilized in CV field for better detection rates.
ANPR can be well utilized by integrating with RFID-systems, GPS, Android platforms and other similar technologies. The Internet-of-Things is beginning to shape future of many industries and is paving new ways for ITS. This inconsistency, challenging environments and other complexities make ANPR an interesting field for researchers. The number plate condition, non-standardized formats, complex scenes, camera quality, camera mount position, tolerance to distortion, motion-blur, contrast problems, reflections, processing and memory limitations, environmental conditions, indoor/outdoor or day/night shots, software-tools or other hardware-based constraint may undermine its performance. Even with the best algorithms, a successful ANPR system deployment may require additional hardware to maximize its accuracy. ANPR technology has the ability to detect and recognize vehicles by their number-plates using recognition techniques.
This paper presents a detailed survey of current techniques and advancements in Automatic-Number-Plate-Recognition (ANPR) systems, with a comprehensive performance comparison of various real-time tested and simulated algorithms, including those involving computer vision (CV). Technologies and services towards smart-vehicles and Intelligent-Transportation-Systems (ITS), continues to revolutionize many aspects of human life.