Locality Recognition Of Vehicle Number Plate using Image Processing Technique

computer engineering

In this research, The matching process for number plate recognition is modified with the help of multi-class RBF neural network optimization. An adaptive threshold method is applied to overcome the dynamic changes of illumination conditions when converting the image into binary. Connected component analysis technique (CCAT) is used to detect candidate objects inside the unknown image. A scale-invariant geometric relationship matrix is introduced to model the layout of symbols in any LP that simplifies system adaptability when applied in different countries. Moreover, two new crossover operators, based on sorting, are introduced, which greatly improve the convergence speed of the system. Color (RGB) to grayscale (GS)conversion is performed using the Filtering technique by eliminating the hue and saturation information while retaining the luminance Most of the CCAT problems, such as touching or broken bodies Edge-based techniques were also implemented to detect the plate based on the high density of vertical edges inside it Detecting license text and at the same time distinguishing it from similar patterns based on the geometrical relationship between the symbols constituting the license numbers is the selected approach in this research. These plates usually contain different colors, are written in different languages, and use different fonts. some plates may have a single color background and others have background images.

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