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\title{Recognition of Similar Shaped Handwritten Marathi Characters Using Artificial Neural Network}
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             \author[1]{virendra  nikam}

             \author[2]{  Prof.Mukesh}

             \author[3]{  Prof.Mukesh}

             \affil[1]{  }

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\date{\small \em Received: 6 December 2011 Accepted: 3 January 2012 Published: 15 January 2012}

\maketitle


\begin{abstract}
        


The growing need have handwritten Marathi character recognition in Indian offices such as passport, railways etc has made it vital area of a research. Similar shape characters are more prone to misclassification. In this paper a novel method is provided to recognize handwritten Marathi characters based on their features extraction and adaptive smoothing technique. Feature selections methods avoid unnecessary patterns in an image whereas adaptive smoothing technique form smooth shape of charecters. Combination of both these approaches leads to the better results. Previous study shows that, no one technique achieves 100% accuracy in handwritten character recognition area. This approach of combining both adaptive smoothing & feature extraction gives better results (approximately 75-100) and expected outcomes.

\end{abstract}


\keywords{Character recognition, features Extraction, adaptive Smoothing, Image segmentation, pattern matching, Image Pixel Rating.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
n recent years, handwritten Marathi character recognition has grabbed a lot of attention as Marathi being primary official language in Maharashtra has wide application in areas like passport, railways, postal address reading etc. Handwritten character recognition \hyperref[b3]{[4]} consist of six main steps. 
\section[{i.}]{i.}\par
Handwritten drawn of character ii.\par
Training on handwritten characters. iii.\par
Testing on handwritten characters. ? Pixel rating an Image ? Image smoothing ? Features extraction. ? Image segmentation. ? Patterns matching ? Result display as per their \% of pattern matched.\par
Earlier, traditional classifiers such as Nearest Neighbor (NN), Hidden Markov Models (HMM) etc. were adopted for character recognition, however they exhibit certain limitations. Machine learning (ML) algorithms \hyperref[b5]{[6]} provide a promising alternative in character recognition based on the feature set given to them. Each character image sample can be expressed in terms of some 26\par
Author ? : M.E (Scholar), Prof.Ram Meghe Institute of Research \& Technology, Badnera. E-mail : apjane@rediffmail.com Author ? : Associate Prof. Prof. Ram Meghe Institute of Research \& Technology, Bandera. E-mail : pundmukesh@rediffmail.com Computer Science \& Information Technology (CS \& IT) quantifiable attributes called features. A variety of features can be extracted such as primitives, profiles etc. Multi Layer (ML) algorithm is then trained with this list of measured features, so that it maps these input features onto a class among certain predefined classes \hyperref[b0]{[1,}\hyperref[b1]{2]}. Then the classifier can be used to determine the class of unknown samples used for testing.\par
ii. 
\section[{Literature survey}]{Literature survey}\par
Character recognition task has been attempted through many different approaches like template matching, statistical techniques like NN, HMM, Quadratic Discriminant function (QDF) etc. Template matching works effectively for recognition of standard fonts, but gives poor performance with handwritten characters and when the size of dataset grows. It is not an effective technique if there is font discrepancy \hyperref[b3]{[4]}. HMM models achieved great success in the field of speech recognition in past decades, however developing a 2-D HMM model for character recognition is found difficult and complex \hyperref[b4]{[5]}. NN is found very computationally expensive in recognition purpose \hyperref[b5]{[6]}. N. Araki et al. \hyperref[b6]{[7]} applied Bayesian filters based on Bayes Theorem for handwritten character recognition. Later, discriminative classifiers such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) grabbed a lot of attention. In \hyperref[b2]{[3]} G. Vamvakas et al. compared the performance of three classifiers: Naive Bayes, K-NN and SVM and attained best performance with SVM. However SVM suffers from limitation of selection of kernel. ANNs can adapt to changes in the data and learn the characteristics of input signal \hyperref[b7]{[8]}.Also, ANNs consume less storage and computation than SVMs \hyperref[b8]{[9]}. Mostly used classifiers based on ANN are MLP and RBFN. B.K. Verma \hyperref[b9]{[10]} presented a system for HCR using MLP and RBFN networks in the task of handwritten Hindi character recognition. The error back propagation algorithm was used to train the MLP networks. J. Sutha et al. in \hyperref[b10]{[11]} showed the effectiveness of MLP for Tamil HCR using the Fourier descriptor features. R. Gheroie et al. in \hyperref[b11]{[12]} proposed handwritten Farsi character recognition using MLP trained with error back propagation algorithm. Computer Science \& Information Technology (CS \& IT) 27 similar shaped characters are difficult to differentiate because of very minor variations in their structures. In \hyperref[b12]{[13]} T. Wakabayashi et al. proposed an F-Ratio (Fisher Ratio) based feature extraction method to improve results of similar shaped characters. They considered pairs of similar shaped characters of different scripts like English, Arabic/Persian, Devnagri, etc. and used QDF for recognition purpose. QDF suffers from limitation of minimum required size of dataset. F. Yang et al. in \hyperref[b13]{[14]} proposed a method that combines both structural and statistical features of characters for similar handwritten Chinese character recognition. As it can be seen that various feature extraction methods and classifiers have been used for character recognition by researchers that are suitable for their work, we propose a novel feature set that is expected to perform well for this application. In this paper, the features are extracted on the basis of character geometry, which are then fed to each of the selected ML algorithms for recognition of SSHMC.\par
iii. 
\section[{Proposed method}]{Proposed method}\par
In this paper, we proposed a novel method based on combinations on pixel rating an image, feature extractions and Image pattern matching. Proposed method gives considerable expected outputs than previous proposed character recognition algorithms like HMM, NN, ML etc. Proposed method consists of following phases.   {\ref -----------------}  ----------Eq.3.  From the study of Literature survey and proposed method, we conclude that, proposed method gives considerable and expected accuracy than previous character recognitions techniques like HMM, ML, NBP etc. Experiment results shows that, proposed method achieved an accuracy nearer to 98\%provided no. of training samples per standard Marathi images should be maximum as possible as.\par
In the process of recognizing handwritten character, human brains may fails that's why to keep an expectations to achieve 100\% accuracy is not expectable. A future work is needed to correctly analyze segments patterns and fuzzy rules mentioned in an equation 3.5.1 to achieve better accuracy which should be independent of no. of training set images. \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-4.png}
\caption{\label{figure4}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.4646666666666666\textwidth}P{0.3853333333333333\textwidth}}
Input handwritten image\tabcellsep Input binary image\\
Input binary image\tabcellsep Pixel Rate Image\end{longtable} \par
 
\caption{\label{tab_0}Table 3 .2.2.1}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.85\textwidth}}
D D D D )\\
(\end{longtable} \par
  {\small\itshape [Note: D 2012 Year From the table 3.2.2.2, it is observed that having pixels]} 
\caption{\label{tab_1}Table 3 .2.2.2}\end{figure}
 			\footnote{© 2012 Global Journals Inc. (US)} 		 		\backmatter  			  				\begin{bibitemlist}{1}
\bibitem[Mitchell (1997)]{b0}\label{b0} 	 		\textit{},  		 			T M Mitchell 		.  	 	 		\textit{Machine Learning}  		December 1997.  	 
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\bibitem[Arica and Yarman-Vural ()]{b7}\label{b7} 	 		‘An Overview Of Character Recognition Focused On Off-line Handwriting’.  		 			N Arica 		,  		 			F T Yarman-Vural 		.  	 	 		\textit{IEEE Transactions on Systems, Man, and Cybernetics}  		2001. 31 p. .  	 
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\end{document}
