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Street view number detection is falling into the category of the natural scene text recognition problem which is quite different from printed character or handwritten recognition problem. Research in this field was started in 90’s but still, it is considered as an unsolved problem. As I mentioned earlier that its difficulties are due to fonts variation, scales, rotations, low lights etc. In earlier years to deal with this matter sequentially, character classification by sliding window * from 4 or connected components * from 4 mainly used. After that word prediction can be done by predicting character classifier in left to right manner. Recently * from 4 segmentation method guided by supervised classifier use where words can be recognized through a sequential beam search. * from 4 But none of this can help to solve the street view recognition problem.In recent works convolutional neural networks proves its capabilities more accurately to solve object recognition task. * from 4 Some research has done with CNN to tackle scene text recognition tasks. * from 4 on that studies CNN shows its huge capability to represent all types of character variation in the natural scene and till now it is holding this highly variability. Analysis with convolutional neural network stars at early 80’s and it successfully applied for handwritten digit recognition in 90’s * from 4 After that with the increasing availability of computer resources, training sets, advance algorithm * from 3 and dropout training *from 3 has to lead to many successes using deep convolutional neural networks.  Previously CNN used mainly for detecting a single object from an input image. It was quite difficult to isolate each character from a single image and identify them. Goodfellow * from 4 solve this problem by a deep large CNN directly to model the whole image and a simple graphical model as top inference layer. The rest of the paper is designed in section III Convolutional neural network architecture, section IV Experiment, Result, and Discussion and Future Work and Conclusion in section V

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