Detection of the vehicle license plate using a kernel density with default search radius algorithm filter

With the contemporary improvements in intelligent transportation systems, automatic car License Plate Detection and Recognition (LPDR) has involved extensive research interests. It has a variety of potential applications in security and traffic control, and much work has been completed on the subject of LPDR. But, majority of prevailing procedures work well either at measured circumstances or with sophisticated image capture systems. It is still a challenging task to read license plates precisely in an open environment. The problem exists in extreme assortment in pattern of characters, such as dissimilar sizes, letterings, misrepresentation, obstruction or blurring, as well as high intricate backgrounds, similar to the common fonts in boards of shops, windows, handrails or construction blocks. In this study, a hybrid methodology has been established in recognizing license plates of candidates in a specific area. This research proposes a technique called kernel density function along with the binary technique that has been utilized for pre-processing. By means of the filtered binary value of the image, the position of car plate can be found by multiplying the binary value and original value of the image. The proposed method outperforms advanced techniques by a huge margin concerning about accuracy in detection as well as efficiency of run-time. The proposed method achieved 98.1 % accuracy to detect the License Plate (LP) and the computation cost is about 0.452 s.