Street But none of this can help
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 must lead to many successes using deep convolutional neural
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