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Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective. Precise controlling and management of traffic conditions, increased safety and surveillance, and enhanced incident avoidance and management should be top priorities in smart city management. At the same time, Vehicle License Plate Number Recognition (VLPNR) has become a hot research topic, owing to several real-time applications like automated toll fee processing, traffic law enforcement, private space access control, and road traffic surveillance. Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number plates. The current research paper presents an effective Deep Learning (DL)-based VLPNR called DL-VLPNR model to identify and recognize the alphanumeric characters present in license plate. The proposed model involves two main stages namely, license plate detection and Tesseract-based character recognition. The detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 model. Then, the characters in the detected number plate are extracted using Tesseract Optical Character Recognition (OCR) model. The performance of DL-VLPNR model was tested in this paper using two benchmark databases, and the experimental outcome established the superior performance of the model compared to other methods.

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Cryptographic image block encryption schemes play a significant role in information enabled services. This paper proposes an image block encryption scheme based on a novel three stage selection (TSS) method in a public cloud with reversible cellular automata. Due to the openness of public cloud, different attacks are possible over user sensitive information. The TSS method has three stages and they generate a robust master key with user plaintext as input and produces an encrypted block as key to be sent to authenticated users. An analysis of experimental results shows that this new method has a large key space and immune to brute force attacks, statistical cryptanalysis attacks and chosen plaintext attacks. Also, the encrypted image entropy value could be increased to 7.9988 making it ideal for a best image block encryption for key generation.

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