LOCALIZATION OF LICENSE PLATE NUMBER USING ANFIS AND GENETIC ALGORITHM

      This project designed based on the paper "Localization of License Plate Number Using Dynamic Image Processing Techniques and Genetic Algorithms" and compared with Adaptive Network-based Fuzzy Inference System (ANFIS).The detection stage of the license plate (LP) is the most critical step in an automatic vehicle identification system. In this design of two methods are used to detect the locations of the license plate (LP) symbols i) Genetic Algorithm (GA) is introduced and ii) ANFIS method. 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. Most of the CCAT problems, such as touching or broken bodies, are minimized by modifying the GA to perform partial match until reaching an acceptable fitness value. The system is implemented using MATLAB and various image samples are experimented with to verify the distinction of the proposed system. The results of the two methods are compared and overall accuracy is reported for different images having variability in orientation, scaling, plate location, illumination, and complex background. Based on these parameters we found Genetic Algorithm provides better results compared to ANFIS.

Reference Paper: Localization of License Plate Number Using Dynamic Image Processing Techniques and Genetic Algorithms

Author’s Name: G. Abo Samra and F. Khalefah

Source: IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION

Year:2014

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SIMULATION VIDEO DEMO                                                                                                                                     


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