Multiple License Plates Detection in Videos and Still Images Using Various Geometrical Properties and Filtering Techniques
Advanced Aspects of Engineering Research Vol. 3,
22 February 2021,
Most of the existing license plate (LP) detection systems have shown significant development in processing of images, with restrictions related to environmental conditions and plate variations. The environmental conditions include different illumination, weather, and background conditions. The plate variations include location of the plate anywhere on the vehicle, many plates in single image, different combination of vehicles with different plate orientations, different sizes of plates, background colour of plates, plates with dirt, rotated plates, LPs having two lines of characters and tilted plates. With increased mobility and internationalization, there is a need to develop a universal LP detection system, which can handle LPs of any country and any vehicle, including motor cycles, in an open environment and all weather conditions. This paper presents a novel LP detection method using different clustering techniques, based on geometrical properties of the LP characters and proposed new character extraction method, for missed character components of LP due to presence of noise between LP characters and LP border. The proposed method detects the number plate of any type of vehicle (including vans, cars, trucks, motorcycles etc.), having different plate variations, under different environmental and weather conditions because of geometrical properties of set of characters in LP.
The proposed method is independent of colour, rotation, and scale variances of LP. The concept is tested using publicly available standard media-lab and Application Oriented License Plate (AOLP) benchmark LP recognition databases. The success rate of the proposed approach for LP detection using media-lab database is 97.3% and using AOLP database is 93.7%. Results clearly indicate that the proposed approach is comparable to the previously published papers, which evaluated their performance on publicly available benchmark databases.