  License plate scanners have grown in popularity in parking lots during the
past few years. In order to quickly identify license plates, traditional plate
recognition devices used in parking lots employ a fixed source of light and
shooting angles. For skewed angles, such as license plate images taken with
ultra-wide angle or fisheye lenses, deformation of the license plate
recognition plate can also be quite severe, impairing the ability of standard
license plate recognition systems to identify the plate. Mask RCNN gadget that
may be utilised for oblique pictures and various shooting angles. The results
of the experiments show that the suggested design will be capable of
classifying license plates with bevel angles larger than 0/60. Character
recognition using the suggested Mask R-CNN approach has advanced significantly
as well. The proposed Mask R-CNN method has also achieved significant progress
in character recognition, which is tilted more than 45 degrees as compared to
the strategy of employing the YOLOv2 model. Experiment results also suggest
that the methodology presented in the open data plate collecting is better than
other techniques (known as the AOLP dataset).
