Recognizing architecture with different pooling methods and multistage

      Recognizing character and digit from
documents such as photographs captured at a street level is a very important
factor in developing digital map. For example, google street view images included
millions of geo-located images. By recognizing images, we can develop a precise
map which can improve navigation services. Though normal character classification
is already solved by computer vision, but still recognizing digit or character
from the natural scene like photographs are still a complex issue.  The reason behind this problem are non-contrasting
backgrounds, low resolution, blurred images, fonts variation, lighting etc.

Traditional
approach of doing this work was a two-step process. First slice the image to
isolate each character and then perform recognition on extracted image. This used
to be done using multiple hand-crafted features and template matching. 1

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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. 5 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. 5 To achieve
the goal, I developed an application which will detect the number from images.
A convolutional neural network model with multiple layers is used to train the
dataset and detect the house digit numbers. I used the traditional
convolutional architecture with different pooling methods and multistage
features and finally got almost 92% accuracy.

 

     Street view number detection is called natural
scene text recognition problem which is quite different from printed character
or handwritten recognition. Research in this field was started in 90’s, but
still it is considered as an unsolved issue. As I mentioned earlier that the
difficulties arise due to fonts variation, scales, rotations, low lights etc.

     In earlier years to deal with natural
scene text identification sequentially, first character classification by
sliding window or connected components mainly used. 4 After that word
prediction can be done by predicting character classifier in left to right
manner. Recently segmentation method guided by supervised classifier use where
words can be recognized through a sequential beam search. 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. 4 Some research has done with CNN to tackle scene text recognition
tasks. 4 Studies on CNN shows its huge capability to represent all types of
character variation in the natural scene and till now it is holding this high
variability. Analysis with convolutional neural network stars at early 80’s and
it successfully applied for handwritten digit recognition in 90’s. 4 With the
recent development of computer resources, training sets, advance algorithm and
dropout training deep convolutional neural networks become more efficient to recognize
natural scene digit and characters. 3

      Previously CNN used mainly to detecting a
single object from an input image. It was quite difficult to isolate each
character from a single image and identify them. Goodfellow et al., solve this
problem by using deep large CNN directly to model the whole image and with a
simple graphical model as the top inference layer. 4

      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.

      Convolutional
Neural Networks (CNN) is a multilayer network to handle complex and
high-dimensional data, its architecture is same as typical neural networks. 8
Each layer contains some neuron which carries some weight and biases. Each
neuron takes images as inputs, then move onward for implementation and reduce
parameter numbers in the network. 7 The first layer is a convolutional layer.
Here input will be convoluted by a set of filters to extract the feature from
the input. The size of feature maps depends on three parameters: number of
filters, stride size, padding. After each convolutional layer, a non-linear
operation, ReLU use. It converts all negative value to zero. Next is pooling or
sub-sampling layer, it will reduce the size of feature maps. Pooling can be different
types: max, average, sum. But max pooling is generally used. Down-sampling also
controls overfitting. Pooling layer output is using to create feature
extractor. Feature extractor retrieves selective features from the input
images. These layers will have moved to fully connected layers (FCL) and the
output layer. In CNN previous layer output considers as next layer input. For the
different type of problem, CNN is different.

 

      The
main objective of this project is detecting and identifying house-number signs
from street view images. The dataset I am considering for this project is
street view house numbers dataset taken from 5 has similarities with MNIST
dataset. The SVHN dataset has more than 600,000 labeled characters and the
images are in .png format. After extract the dataset I resize all images in
32×32 pixels with three color channels. There are 10 classes, 1 for each digit.
Digit ‘1’ is label as 1, ‘9’ is label as 9 and ‘0’ is label as 10. 5 The
dataset is divided into three subgroups: train set, test set, and extra set.
The extra set is the largest subset contains almost 531,131 images.
Correspondingly, train dataset has 73,252 and test data set has 26,032 images.

 

     Figure 3 is an example of the original,
variable-resolution, colored house-number images where each digit is marked by
bounding boxes. Bounding
box information is stored in digitStruct.mat file, instead of drawn directly on
the images in the dataset. digitStruct.mat file contains a struct called
digitStruct with the same length of original images. Each element in
digitStruct has the following fields: “name” which is a string containing the
filename of the corresponding image. “bbox” is a struct array that contains the
position, size, and label of each digit bounding box in the image. As an example,
digitStruct(100). bbox (1). height means
the height of the 1st digit bounding box in the 100th image. 5

 

This is very clear
from Figure 3 that in SVHN dataset maximum house numbers signs are printed
signs and they are easy to read. 2 Because there is a large variation in
font, size, and colors it makes the detection very difficult. The variation of
resolution is also large here. (Median: 28 pixels. Max: 403 pixels. Min: 9
pixels). 2 The graph below indicates that there is the large variation in
character heights as measured by the height of the bounding box in original
street view dataset. That means the size of all characters in the dataset,
their placement, and character resolution is not evenly distributed across the
dataset. Due to data are not uniformly distributed it is difficult to make
correct house number detection

 

 

 In my experiment, I train a multilayer CNN for
street view house numbers recognition and check the accuracy of test data. The
coding is done in python using Tensorflow, a powerful library for
implementation and training deep neural networks. The central unit of data in
TensorFlow is the tensor. A tensor consists of a set of primitive values shaped
into an array of any number of dimensions. A tensor’s rank is its number of
dimensions. 9 Along with TensorFlow used some other library function such as
Numpy, Mathplotlib, SciPy etc.

 

I perform my
analysis only using the train and test dataset due to limited technical resources.
And omit extra dataset which is almost 2.7GB. To make the analysis simpler delete
all those data points which have more than 5 digits. By preprocessing the data
from the original SVHN dataset a pickle file is created which being used in my
experiment. For the implementation, I randomly shuffle valid dataset and then
used the pickle file and train a 7-layer Convoluted Neural Network.

 

At the very
beginning of the experiment, first convolution layer has 16 feature maps with
5×5 filters, and originate 28x28x16 output. A few ReLU layers are also added
after each convolution layer to add more non-linearity to the decision-making
process. After first sub-sampling the output size decrease in 14x14x10. The
second convolution has 512 feature maps with 5×5 filters and produces 10x10x32
output. By applying sub-sampling second time get the output size 5x5x32.
Finally, the third convolution has 2048 feature maps with same filter size. It
is mentionable that the stride size =1 in my experiment along with zero padding.
During my experiment, I use dropout technique to reduce the overfitting.
Finally, SoftMax regression layer is used to get the final output.

Weights are
initialized randomly using Xavier initialization which keeps the weights in the
right range. It automatically scales the initialization based on the number of
output and input neurons. After model buildup, start train the network and log
the accuracy, loss and validation accuracy for every 500 steps.Once the process
is done then get the test set accuracy. To minimize the loss, Adagrad Optimizer used.
After reach in a suitable accuracy level stop train the network and save the
hyperparameters in a checkpoint file. When we need to perform the detection, the
program will load the checkpoint file without train the model again.

Initially,
the model produced an accuracy of 89% with just 3000 steps. It’s a great
starting point and certainly, after a few times of training the accuracy will reach
in 90%. However, I added some additional features to increase accuracy. First, added
a dropout layer between the third convolution layer and fully connected layer. This
allows the network to become more robust and prevents overfitting. Secondly, introduced
exponential decay to calculate learning rate with an initial rate 0.05. It will
decay in each 10,000 steps with a base of 0.95. This helps the network to take
bigger steps at first so that it learns fast but over time as we move closer to
the global minimum, it will take smaller steps. With these changes, the model
is now able to produce an accuracy of 91.9% on the test set. Since there are a
large training set and test set, there is a chance of more improvement if the
model will train for a longer time.

During my
analysis, I reached an accuracy level of almost 92%. After train the model first
time the accuracy was 89%. After several times of training it reached to 92%. As
mentioned earlier that the saved checkpoint file will be restored later to
continue training or to detect new images. By using the dropout, its confirm
that the model is suitable and can predict most images. The model is tested
over a wide range of input from the test dataset.

 

     

To recognize house
numbers this model can detect most of the images. From Figure 5 it appears that
among ten house numbers it correctly recognizes seven house numbers. However,
the model still gives incorrect output when the images are blurry or has any
other noise. Due to limited resource I train the model few times as it takes
longer time to run. I believe there is a strong possibility to increase the
accuracy level if work with whole dataset. Also, the use of better hardware and
GPU can run the model faster.

In the experiment I proposed a multi-layer deep
convolutional neural network to recognize the street view house number. The testing
done on more than 600,000 images and achieve almost 92% accuracy. From the
analysis it is vibrant that the model produces correct output for most images.
However, the detection may fail if the Image is blurry, or contain any noise.

 Most exciting feature
of the project is to discover the performance of some applied tricks like
dropout and exponential learning rate decay on real data. As many variation of
CNN architecture can be implemented, it’s very difficult to understand which architecture
will work best for any specific dataset. Determine the most appropriate CNN
architecture was very challenging aspect of this experiment. The model
implemented in this project is relatively simple but does the job very well and
is quite robust. However still some works need to be done to optimize accuracy
level. As a future work, I will extend my experiment using another architecture
of CNN along with hybrid technique and algorithms. And try to find out which
one gives better accuracy with minimum cost and less number of loss. As well as
try to incorporate the whole dataset in next experiment.

 

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