Toward End-to-End Car License Plate Detection and Recognition With Deep Neural Networks

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Aim:

The aim of this project is to implement intelligent urban surveillance system for automated Number plate Recognition

Existing System:

The system which uses computer vision to address these problem were using a single central computing server to process the collected images using cameras scattered across the smart city. A single unified deep neural network is proposed, which can detect license plates from an image and recognize the labels all at once. The whole framework involves no heuristic processes, such as the use of plate colors or character space, and avoids intermediate procedures like character grouping or separation.

Proposed System:

Here we are proposing a unique combination of technologies which were available in the market. We are proposing to do the image processing in the local environment. The images from the camera were processed near the camera itself and the results were published to the central server for further processing. The scope of this project is to present a cost effective viable solutions, so we will be implementing the system and technologies needed to process the image locally and convolution neural network (CNN) techniques used in detecting the number plate region.