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\title{Critique of Various Algorithms for Handwritten Digit Recognition using Azure ML Studio}
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             \author[1]{Goutham  Cheedella}

             \affil[1]{  Narasaraopeta Engineering College}

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\date{\small \em Received: 11 December 2019 Accepted: 31 December 2019 Published: 15 January 2020}

\maketitle


\begin{abstract}
        


Handwritten Digit Recognition is probably one of the most exciting works in the field of science and technology as it is a hard task for the machines to recognize the digits which are written by different people. The handwritten digits may not be perfect and also consist of different flavors. And there is a necessity for handwritten digit recognition in many real-time purposes. The widely used MNIST dataset consists of almost 60000 handwritten digits. And to classify these kinds of images, many machine learning algorithms are used. This paper presents an in-depth analysis of accuracies and performances of Support Vector Machines (SVM), Neural Networks (NN), Decision Tree (DT) algorithms using Microsoft Azure ML Studio.

\end{abstract}


\keywords{handwritten recognition, digit recognition, azure machine learning studio, neural networks, support vector machines, decision tree, classification}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
andwritten Digit Recognition is probably one of the most exciting works in the field of science and technology as it is a hard task for the machines to recognize the digits which are written by different people. The handwritten digits may not be perfect and also consist of different flavors. And there is a necessity for handwritten digit recognition in many real-time purposes. The widely used MNIST dataset consists of almost 60000 handwritten digits. And to classify these kinds of images, many machine learning algorithms are used. This paper presents an in-depth analysis of accuracies and performances of Support Vector Machines (SVM), Neural Networks (NN), Decision Tree (DT) algorithms using Microsoft Azure ML Studio.\par
For many years, we are using handwritten digit recognition in several ways, and even though there is much advancement made in the system, there are minute errors in recognizing the digits. Still, we haven't achieved 100\% accuracy, and research is going on. Seldom 1\% or 2\% errors may also lead to inapt results in several real-time applications.\par
And here, we have used the MNIST dataset for the training and testing of our model. In this dataset, it consists of almost 60000 images which are already normalized and centered. And so each image is of size 28x28, which forms an array size of 784 pixels with values ranging from 0 to 255. Whenever a pixel value is '1', it represents that the background is white, and for black it is '0'. 
\section[{II.}]{II.} 
\section[{Implementation a) A glimpse of the MNIST Dataset}]{Implementation a) A glimpse of the MNIST Dataset}\par
The handwritten digit recognition is a broad research topic which gives an extensive survey of the field, including significant feature sets, learning datasets, and algorithms. The MNIST stands for the Modified National Institute of Standards and Technology dataset. It is a dataset of 60000 small square 28×28 pixel grayscale images of handwritten single digits between 0 and 9.\par
It helps to train various image processing systems and for training and testing in the field of machine learning. It was created by remixing the samples from NIST's original datasets which stands for the National Institute of Standards and Technology, a unit of the U.S. Commerce Department.\par
There are several scientific papers on attempts to achieve the lowest error rate as possible. An extended dataset which is similar to MNIST called EMNIST published in 2017, which contains 240000 training images, and 40000 testing images of handwritten digits and characters.  
\section[{b) Support Vector Machines}]{b) Support Vector Machines}\par
Support Vector Machines helps us to find a hyperplane in N-dimensional space (N-the number of features), which classifies the data points distinctly. It is a supervised machine learning model used for classification and regression. In this, the data points get classified applying the concept of the hyper plane. Its dimensions may vary depending upon the number of features. 
\section[{Figure 2: Support Vector Machine Classifier}]{Figure 2: Support Vector Machine Classifier}\par
The separation margin should be of equal distance from the classes. SVM offers notable immeasurable features for the classification problem. Initially, the MNIST dataset gets loaded into the Azure ML studio, and once the data preprocessing gets completed, the model training takes place using the SVM algorithm. Then the labels are predicted for the given inputs. Finally, we attain the accuracy, precision, recall, and F1 scores.\par
Using the two-class boosted SVM, we got an accuracy of 91\% on the MNIST dataset.\par
In SVM, it's always wise to scale the input data so that the training time becomes less and also the model rendering takes more time if the data is more. Image of the SVM results 
\section[{c) Decision Tree Algorithm}]{c) Decision Tree Algorithm}\par
These are used successfully in many diverse areas. A Decision Tree mainly consists of three components, such as nodes: test for the value of a particular attribute, Edge/ Branch: outcome of a test and connect to the next node or leaf and leaf nodes/terminal nodes. The Decision Tree forms from the given input data. At long last, the classification procedure through a tree-like structure is consistently natural and interpretable. The two main types of Decision Trees are classification and regression. In this, we are using the Decision Tree for classification as yes/no types where a tree gets built using a process known as binary recursive partitioning. It is an iterative process of splitting the data into partitions and then splitting it up further on each of the branches. The critical point is to use a decision tree to partition the data into cluster regions and empty regions. Initially, load the dataset into the Azure ML Studio, and after the data preprocessing, the model is trained using the two-class boosted decision tree, and then the scores are obtained.\par
With the use of this classifier, we have achieved an accuracy of 99.5\% on the MNIST dataset. 
\section[{d) Neural Networks}]{d) Neural Networks}\par
The usage of Neural Networks is found more in pattern recognition and image processing systems. They are multi-layer networks of neurons with non-linear mapping structures. For a set of inputs and a set of the target values, predictions made should match those target values as close as possible. A simple model consists of a connection, which transforms input to output and a neuron that includes a bias term and activation function. A positive weight means an excitatory connection, while negative values mean inhibitory connections. All inputs are transformed and then summed. This activity refers to as a linear combination. Subsequently, an activation function controls the amplitude of the output. 
\section[{Figure 4: Neural Networks}]{Figure 4: Neural Networks}\par
The complexity of a model increases as the number of hidden layers increases. And so the prediction capability of the model for better performance also increases.\par
Here we initially load the MNIST dataset into the Azure ML Studio, and then the model is trained using the two-class Neural Networks. Using this, we achieved an accuracy of 99.8\% on the MNIST dataset. 
\section[{III.}]{III.} 
\section[{Experimental Tools}]{Experimental Tools}\par
Azure Machine Learning Studio is a collaborative, drag, and drop tool developed by Microsoft Corporation to build, test, and then to deploy predictive analytics solutions of our data. Azure Machine Learning Studio publishes models also as web services that can easily be consumed by custom applications or tools such as Excel.\par
It is where everything like data science, predictive analytics, our data, and all our cloud resources meet. It is a robust and easy to use platform for deploying several machine learning models. Developing a model using this studio is like an iterative process. As we modify the various functions and their parameters, our results converge until we are satisfied that we have a trained and capable model.\par
It provides an interactive environment where everything is drag and drops supported to build and iterate on a predictive analysis model quickly. By connecting the required components such as datasets, algorithms, and specific analysis models (score model, train model), we can form an experiment. When the training completes, we can publish it as a web service so that others can access it. The Azure ML Studio allows us to experiment on any kind of dataset to construct a predictive analysis model for any system.\par
IV. 
\section[{Results and Discussion}]{Results and Discussion}\par
The scores obtained can be illustrated using the below table  V. 
\section[{Conclusion}]{Conclusion}\par
In this paper, the performances of various algorithms like Support Vector Machines, Decision Trees and Neural Networks have been weighed and analyzed to reveal the best classifier for adequate recognition of handwritten digits using Microsoft Azure Machine Learning Studio. In any recognition process, the important thing is to preprocess the data and then adequately train the model using all necessary measures. Using the Azure Machine Learning Studio, we can do all the processing within less time. \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-3.png}
\caption{\label{fig_1}Figure 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Figure 5 :D}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Figure 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7}\includegraphics[]{image-6.png}
\caption{\label{fig_4}Figure 7 :}\end{figure}
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