1. Barrier Access Control Using Sensors Platform and Vehicle License Plate Characters Recognition
- Author
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Ajmal Khan, Shizra Mehmood, Daehan Kwak, Khalid Mahmood Awan, Sania Niaz, Ata Ur Rehman, Hafeez Anwar, Iram Shahzadi, and Farman Ullah
- Subjects
barrier control ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Access control ,02 engineering and technology ,lcsh:Chemical technology ,computer.software_genre ,Biochemistry ,Article ,Analytical Chemistry ,machine learning algorithms ,0202 electrical engineering, electronic engineering, information engineering ,lcsh:TP1-1185 ,Segmentation ,Computer vision ,Electrical and Electronic Engineering ,Instrumentation ,License ,license plate recognition ,business.industry ,Character (computing) ,020207 software engineering ,Optical character recognition ,Pipeline (software) ,Atomic and Molecular Physics, and Optics ,features extraction ,raspberry-pi ,vehicle detection ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,computer ,sensors platform - Abstract
The paper proposes a sensors platform to control a barrier that is installed for vehicles entrance. This platform is automatized by image-based license plate recognition of the vehicle. However, in situations where standardized license plates are not used, such image-based recognition becomes non-trivial and challenging due to the variations in license plate background, fonts and deformations. The proposed method first detects the approaching vehicle via ultrasonic sensors and, at the same time, captures its image via a camera installed along with the barrier. From this image, the license plate is automatically extracted and further processed to segment the license plate characters. Finally, these characters are recognized with the help of a standard optical character recognition (OCR) pipeline. The evaluation of the proposed system shows an accuracy of 98% for license plates extraction, 96% for character segmentation and 93% for character recognition.
- Published
- 2019