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Automatic Number Plate Recognition has been a topic due to many practical applications. However, lots of the solutions which can be current still not robust in real-world situations, commonly based on many constraints. ANPR systems typically have three stages: Automatic Number Plate detection, character segmentation and character recognition. It need higher precision or almost perfection, since its failed to identify the NP may possibly lead to a failure in the stages that are next. Many approaches search first for the vehicle and then its NP in order to reduce processing time and eliminate positives that are false. Although ANPR has been frequently addressed in the literature, many studies and solu-tions are still not robust enough on real-world scenarios. These systems where dealing with images, the accuracy depends on many parameters like camera, lighting conditions etc. Many computer vision tasks accuracy always depends on feeding huge number of training data or called as dataset. As of these limitations with computer vision, Deep Learning arise.The accuracy and performance of any applications like ANPR using DL gives descent output but still there is a demand of proper dataset annotation.