Foggy Image Clearance and Vehicle Number Plate Identification using Python

Foggy Image Clearance and Vehicle Number Plate Identification using Python

Shreya Sharma1, Sonu Agrawal2

M.Tech Student1, Dept. of Computer Science, Shri Shankaracharya Technical Campus, C.G., Email: bukkusharma@gmail.com

Associate Professor2, Dept. of Computer Science & Engineering, Shri Shankaracharya Technical Campus,

C.G., Email: agrawalsonu@gmail.com

Abstract: Number plate detection algorithm is mainly categorized into three classes: edge- based, colour based and texture based. License plate location algorithm based on edge detection and morphology are describe to locate the number plate, first identify whether any noise is present in the plate. In the field of computer and machine vision, haze and fog lead to image degradation through various degradation mechanisms including but not limited to contrast attenuation, blurring and pixel distortions. This limits the efficiency of machine vision systems such as video surveillance, target tracking and recognition. Various single image dark channel dehazing algorithms have aimed to tackle the problem of image hazing in a fast and efficient manner. Such algorithms rely upon the dark channel prior theory towards the estimation of the atmospheric light which offers itself as a crucial parameter towards dehazing. This paper describes the use of sobel technique and image morphing for number plate detection and ocr technique is used to extract the number of the vehicle

Keywords: Vehicle number plate, sobel, dehazing, image morphing, ocr