  Object Detection is a popular field of research for recent technologies. In
recent years, profound learning performance attracts the researchers to use it
in many applications. Number plate (NP) detection and classification is
analyzed over decades however, it needs approaches which are more precise and
state, language and design independent since cars are now moving from state to
another easily. In this paperwe suggest a new strategy to detect NP and
comprehend the nation, language and layout of NPs. YOLOv2 sensor with ResNet
attribute extractor heart is proposed for NP detection and a brand new
convolutional neural network architecture is suggested to classify NPs. The
detector achieves average precision of 99.57% and country, language and layout
classification precision of 99.33%. The results outperforms the majority of the
previous works and can move the area forward toward international NP detection
and recognition.
