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License plate recognition is a significant technology to build a smart city transportation system. The current license plate recognition systems have achieved a good recognition and application effect for single-row license plates, nonetheless, these can’t satisfy the recognition need for license plates which contain stacked characters. Based on this problem, this paper proposed a method based on deep learning and independent of character segmentation to identify the license plates containing stacked characters. Firstly, the method corrected the tilted or distorted license plate, and used MobileNet-SSD algorithm to detect single-row characters and stacked characters in the license plate. Then, it sent stacked characters to the stacked character recognition network based on CTC loss and conducted the end-to-end identification without character segmentation. Result of the experiment indicates that this algorithm not only has high recognition accuracy for the license plate with stacked characters, but also adapts to the license plate under the complex environment such as tilt and distortion, greatly improves the generality of the license plate recognition system. [ABSTRACT FROM AUTHOR]

For the multi-attribute group decision-making problem in Pythagoras environment, firstly, this paper combined the Pythagorean fuzzy number and the power-average operator to creatively expand a new improved weighted support, then based on this, it proposed a Pythagorean fuzzy interaction power-average operator with improved weighted support, and discussed the properties of the operator. Then it established a decision-making method that could reflect the interaction between decision attributes under Pythagoras fuzzy background. Finally, it was applied to the evaluation of smart cities. The case study shows that the method can solve the problem of actual multi-attribute group decision-making and can be further applied to the fields of intelligent logistics, pattern recognition and artificial intelligence. [ABSTRACT FROM AUTHOR]