Recognizing neural network model with multiple layers is

Recognizing
character and digit from documents such as photographs which captured at a
street level is a very important factor in modern-day map making. For example,
automatically identify an address accurately from street view images of that
building. By using this information more precise map can be built and it can
also improve navigation services. Though normal character classification is
already a solved problem by computer vision but still recognizing digit or
character from the natural scene like photographs are still a harder problem.  The reason behind the difficulties may be the
non-contrasting backgrounds, low resolution, blurred images, fonts variation,
lighting etc.

Traditional
approaches for classifying characters and digits from natural images were
separated into two channels. First segmenting the images to extract isolated
characters and the perform recognition on extracted images. And this can be
done using multiple hand-crafted features *1 er ref and template matching. *1
er ref

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The
main purpose of this project is to recognize the street view house number by
using a deep convolutional neural network. 
For this work, I considered the digit classification dataset of house
numbers which I extracted from street level images.
http://ufldl.stanford.edu/housenumbers/. This dataset is similar in flavor to
MNIST dataset but with more labeled data. It has more than 600,000-digit images
which contain color information and various natural backgrounds and collected
from google street view images. To achieve the goal, I formed an application
which will detect the number of just image pixels. Here, a convolutional neural
network model with multiple layers is used to train the dataset and detect the
house digit number with high accuracy. I used the traditional convolutional
architecture with different pooling methods and multistage features and finally
got 91.1% accuracy.