Automatic number plate recognition system is a mass surveillance embedded system that recognizes the number plate of the vehicle. This system is generally used for traffic management applications. It should be very efficient in detecting the number plate in noisy as well as in low illumination and also within required time frame. This paper proposes a number plate recognition method by processing vehicle's rear or front image. After image is captured, processing is divided into four steps which are preprocessing, number plate localization, character segmentation and character recognition. Preprocessing enhances the image for further processing, number plate localization extracts the number plate region from the image, character segmentation separates the individual characters from the extracted number plate, and character recognition identifies the optical characters by using random forest classification algorithm. Experimental results reveal that the accuracy of this method is 90.9%.

With the introduction of the term blockchain in 2008, it's interest has been increasing in the community since the idea was coined. The reason for this interest is because it provides anonymity, security and integrity without any central third party organisation in control of data and transaction. It has attracted huge interest in research areas due to its advances in various platforms, limitations and challenges. There are various Distributed Ledger Technologies that demonstrates their special features which overcome limitations of other platforms. However, implementations of various distributed ledger technologies differ substantially based on their data structures, consensus protocol and fault tolerant among others. Due to these variations, they have a quite different cost, performance, latency and security. In this paper, working and in-depth comparison of major distributed ledger technologies including their special features, strengths and weaknesses is presented and discussed by identifying various criteria.

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