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\title{Bangla Character Recognition System is Developed by using Automatic Feature Extraction and XOR Operation}
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\begin{document}

             \author[1]{Md. Mojahidul  Islam}

             \author[2]{Md. Imran  Hossain}

             \author[3]{Md. Kislu  Noman}

             \affil[1]{  Islamic University, Bangladesh.}

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\date{\small \em Received: 15 December 2012 Accepted: 4 January 2013 Published: 15 January 2013}

\maketitle


\begin{abstract}
        


This paper presents off-line bangle character recognition system using automatic feature extraction and XOR operation. In this system, the Bangla text is accepted as an image file which is first segmented into lines and words and then each word is segmented into characters. The pixels outside the boundary of the character are eliminated. The characters are scaled to a size equal to the database image. A XOR operation is performed between the scaled image and the database image and the error (%) is calculated. Finally, depending on the minimum error, the system recognizes the character to use in the output. The average recognition accuracy rate of the system was about 80%.

\end{abstract}


\keywords{character recognition; character segmentation; automatic feature extraction; XOR operation.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
II. 
\section[{Implementation of Character Recognition System}]{Implementation of Character Recognition System}\par
The character recognition system can be divided as segmentation of text document into character and recognition of the character. The whole process is shown in  he subject of character recognition has been receiving considerable attention in recent years due to the advancement of the automation process. Automatic character recognition improves the interaction between man and machine in many applications like office automation, cheque verification, mail sorting, and a large variety of banking, business and data entry applications. We are concerned here with the recognition of character in Bangla language. Bangla is the mother language of Bangladesh and approximately 10\% of the world's population speaks in Indian, Chinese and other languages trying to develop the complete character recognition system. In our country, research works in this field have achieved a limited success so far as compared to the other foreign languages. Though, the achievement in this fascinating field is not enough to reach the ultimate goal. But the progress of such research with Bangla language is still in an initial level. This research is a simple flourish to T implement that dream as the initial step to convert the Bangla text to computer readable form that is development of complete Bangla Character Recognition system. Individual Bangla characters were recognized using various techniques such as geometric shape analysis, black runs and concavity measurement technique. The input images are acquired from documents containing text by using scanner as an input device or using Adobe Photoshop or Paint. Acquired images are then stored in Hard Disk in JPG picture format. This image is then passed for preprocessing. 
\section[{Source}]{Source} 
\section[{b) Pre-Processing}]{b) Pre-Processing}\par
The scanned image is converted into binary image. At first, the RGB image is converted into grayscale image and then binary image i.e. an image with pixel 0 (white) and 1 (black). After converting the image, the unnecessary pixels (0s) from the original image is removed. 
\section[{c) RGB to Grayscale and Gray to RGB Conversion}]{c) RGB to Grayscale and Gray to RGB Conversion}\par
In practical cases most of the images are generally color (RGB), but it is complex to work with a three-dimensional array. So it needs to convert the RGB image into the grayscale image. The RGB to grayscale conversion is performed by MATLAB command. 
\section[{I = rgb2gray(f)}]{I = rgb2gray(f)}\par
For ease of analysis, the grayscale image is converted into binary image by using the following MATLAB command. BW = im2bw(I)III. 
\section[{Text Segmentation}]{Text Segmentation}\par
Text segmentation is a process where the text is partitioned into its elementary entities i.e. characters \hyperref[b8]{[10]}. The total performance of the character recognition process depends on the accuracy of the segmentation process of the text into the characters. In the segmentation phase, first the document is segmented into text lines, the text lines are segmented into text words and then the words are segmented into characters. 
\section[{a) Line Segmentation}]{a) Line Segmentation}\par
Text line segmentation is performed by scanning the input image horizontally. Frequency of black pixels in each raw is counted to separate the line. The position between two consecutive lines, where the number of black pixels in a raw is zero denotes a boundary between the lines \hyperref[b10]{[13]}. The output image is shown in  In English text there is a minimum gap between two consecutive characters and two consecutive words. The minimum gap between two consecutive words is greater than two consecutive characters. Although maximum characters in Bangla text line are connected by matra line with each other, the same case occurs if the gap exists between them. For word segmentation from the text line, the vertical scan is performed. If there exists n consecutive scan that find no black pixel, we denote it to be a marker between two words. The value of n is the minimum gap between two consecutive words which is taken experimentally. The output is shown in For character segmentation from the word, the vertical scan is performed. The starting boundary of a character is the first column where the first black is found. After finding the starting boundary of a character, it continues scanning until a column without any black pixel is found, which is the ending boundary of the character being processed \hyperref[b11]{[14]}. Fig. \hyperref[fig_6]{4} shows a single segmented character and its corresponding binary format. The knowledge base is designed based on the feature matrix of various characters. In order to build the knowledge base, first, the RGB character image is converted into grayscale image then it is converted into binary image. After getting the binary image, the unnecessary pixels from the character boundary is eliminated. 
\section[{e) Feature Extraction}]{e) Feature Extraction}\par
Feature extraction is the process of extracting essential information content from the image segment. It plays an important role in the whole recognition process \hyperref[b8]{[10]}. 
\section[{f) Scaling}]{f) Scaling}\par
Depending on the height and width of the database image the segmented characters are scaled. If the size of the segmented character is higher than the database character then the system will be scaled down all the segmented characters to the size of the database character, otherwise scaled up. If C be the segmented character then the scaled image S is obtained by the following MATLAB command: S = imresize(C, [height, width]).\par
Where, height and width is the dimension of the database character. Character recognition performance depends on the scaling. If the segmented character is too higher or too lower than the database image then the character recognition performance is reduced. The character recognition procedure is described in following Algorithm: BEGIN 1. Calculate total pixel = height×width. 2. Take XOR between first database character and scaled character S. 3. Calculate no. of correct pixels (0 is the correct pixel), correct pixel. \hyperref[b4]{4}  In this way, for all database character the error (\%) calculation is repeated. If the database character exactly or approximately matches with the segmented character then the error (\%) will minimum. So base on the minimum error, the system gives the corresponding output character. 
\section[{IV. Result and Performance Analysis}]{IV. Result and Performance Analysis}\par
The system is divided in two main phases: segmentation and character recognition. So the overall performance of the system directly depends on the performance of the two individual phases. The accuracy of this system is measured as the success rate for the recognition of characters. It is measured using Eq. (  {\ref 1} The segmentation performance of this system is shown in Table \hyperref[tab_2]{1}.  V. 
\section[{Discussion and Conclusion}]{Discussion and Conclusion}\par
The aim of this system is to recognize Bangla characters. This system can recognize these characters with slight limitations. The limitations are discussed in the following section. 
\section[{a) Limitation}]{a) Limitation}\par
The performance of this system depends on the segmentation and recognition. If the characters of text are in very close or overlap to each other, then the system fails to segment the characters. For Bangla characters, different font size is possible in practical. It is not possible to store all the front size in database. So it needs to scale the character which causes distortion in character shape. It should create a problem but the system should not fail always. b) Further Scope Due to the limitations described in previous section the system is not suitable for on-line applications. The overlapping character can be segmented by using Flood fill and Boundary fill algorithm. It is further target to perform this work. 
\section[{c) Conclusion}]{c) Conclusion}\par
In this paper the off line bangle character recognition system is developed by using automatic feature extraction and XOR operation. The efficiency of this system is not so high. In future, MLP and SVM classifier can be used for character recognition. \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-3.png}
\caption{\label{fig_2}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-4.png}
\caption{\label{fig_3}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-5.png}
\caption{\label{fig_4}GlobalFigure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-6.png}
\caption{\label{fig_5}Figure 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-7.png}
\caption{\label{fig_6}Figure 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5}\includegraphics[]{image-8.png}
\caption{\label{fig_7}Figure 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6}\includegraphics[]{image-9.png}
\caption{\label{fig_8}Figure 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.3178097345132744\textwidth}P{0.08462389380530973\textwidth}P{0.3347345132743363\textwidth}P{0.11283185840707965\textwidth}}
No. of Lines in a\tabcellsep Line Segmentation\tabcellsep Word Segmentation\tabcellsep Character Segmentation\\
Text Document\tabcellsep Accuracy (\%)\tabcellsep Accuracy (\%)\tabcellsep Accuracy (\%)\\
4\tabcellsep 100\tabcellsep 97\tabcellsep 89.05\\
5\tabcellsep 100\tabcellsep 97.5\tabcellsep 92.50\\
6\tabcellsep 100\tabcellsep 94\tabcellsep 90.71\\
7\tabcellsep 100\tabcellsep 96.67\tabcellsep 92.69\\
8\tabcellsep 100\tabcellsep 94\tabcellsep 90.32\\
\multicolumn{2}{l}{b) Segmented Character Recognition Performance}\tabcellsep \multicolumn{2}{l}{technique. The character recognition performance of}\\
\multicolumn{2}{l}{For character recognition, this system uses}\tabcellsep \multicolumn{2}{l}{this system is shown in Table 2 for Shoroborno and}\\
\multicolumn{2}{l}{XOR operation which is a very simple matching}\tabcellsep \multicolumn{2}{l}{table 3 for Numerical Character.}\end{longtable} \par
 
\caption{\label{tab_2}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.11065088757396449\textwidth}P{0.186094674556213\textwidth}P{0.16094674556213018\textwidth}P{0.23136094674556212\textwidth}P{0.16094674556213018\textwidth}}
No. of Test\tabcellsep Total No. of\tabcellsep Total\tabcellsep Success\tabcellsep Average Success\\
Sample\tabcellsep Characters\tabcellsep No. of Success\tabcellsep Rate (\%)\tabcellsep Rate (\%)\\
1\tabcellsep 120\tabcellsep 90\tabcellsep 75\tabcellsep \\
2\tabcellsep 150\tabcellsep 116\tabcellsep 77.33333\tabcellsep \\
3\tabcellsep 125\tabcellsep 94\tabcellsep 75.2\tabcellsep 76.96368\\
4\tabcellsep 130\tabcellsep 102\tabcellsep 78.46154\tabcellsep \\
5\tabcellsep 170\tabcellsep 134\tabcellsep 78.82353\tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.12064516129032257\textwidth}P{0.17548387096774193\textwidth}P{0.15903225806451612\textwidth}P{0.2193548387096774\textwidth}P{0.17548387096774193\textwidth}}
No. of Test\tabcellsep Total No. of\tabcellsep Total\tabcellsep Success\tabcellsep Average Success\\
Sample\tabcellsep Characters\tabcellsep No. of Success\tabcellsep Rate (\%)\tabcellsep Rate (\%)\\
1\tabcellsep 50\tabcellsep 42\tabcellsep 84\tabcellsep \\
2\tabcellsep 70\tabcellsep 53\tabcellsep 75.71429\tabcellsep \\
3\tabcellsep 65\tabcellsep 56\tabcellsep 86.15385\tabcellsep 83.27363\\
4\tabcellsep 40\tabcellsep 33\tabcellsep 82.5\tabcellsep \\
5\tabcellsep 50\tabcellsep 44\tabcellsep 88\tabcellsep \end{longtable} \par
 
\caption{\label{tab_4}Table 3 :}\end{figure}
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