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This paper proposes an improved Convolutional Neural Network (CNN) algorithm approach for license plate recognition system. The main contribution of this work is on the methodology to determine the best model for four-layered CNN architecture that has been used as the recognition method. This is achieved by validating the best parameters of the enhanced Stochastic Diagonal Levenberg Marquardt (SDLM) learning algorithm and network size of CNN. Several preprocessing algorithms such as Sobel operator edge detection, morphological operation and connected component analysis have been used to localize the license plate, isolate and segment the characters respectively before feeding the input to CNN. It is found that the proposed model is superior when subjected to multi-scaling and variations of input patterns. As a result, the license plate preprocessing stage achieved 74.7% accuracy and CNN recognition stage achieved 94.6% accuracy.

Variance in business process can lead to various changes and modifications of business requirements, strategies and functionalities since it is a valuable source of organizational intellectual capital and represents a preferred and successful work practice. It is important to provide an effective method to analyze the similarity between these variants since it can bring benefits for organization productivity. Through this paper, we propose an efficient approach for undertaking the structural similarity analysis and subsequently providing a formula to compute the degree of similarity between the structural relationships of the variants in a systematic way. We hope that the systematic method introduced in this paper can be applied successfully to solve the ambiguity issue in defining and measuring the structural similarity between the process variant.

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