i-manager Publications

i-manager's Journal on Pattern Recognition (JPR)

Current Issue Vol. 10 Issue 2

Most Cited

Volume 2 Issue 4 December - February 2016 [Open Access]

Research Paper

Local Orientation Gradient XOR Patterns: A New Feature Descriptor for Image Indexing and Retrieval

A. Hariprasad Reddy* , N. Subhash Chandra**

* Research Scholar, Department of Computer Science and Engineering, JNTU Hyderabad, Andhra Pradesh, India.

** Principal & Professor, Holy Mary Institute of Technology and Science, Hyderabad, Andhra Pradesh, India.

Reddy, A. H., and Chandra, N. S. (2016). Local Orientation Gradient XOR Patterns: A New Feature Descriptor for Image Indexing and Retrieval. i-manager’s Journal on Pattern Recognition, 2(4), 1-10. https://doi.org/10.26634/jpr.2.4.5943

Abstract

This paper presents a novel feature extraction method, Local Orientation Gradient XoR Patterns (LOGXoRP) for image indexing and retrieval. The LOGXoRP encodes the exclusive OR (XOR) operation between the center pixel and its surrounding neighbors of quantized orientation and gradient values, whereas the Local Binary Patterns (LBP) and the Local Gradient Patterns (LGP) encode the relationship between the gray values of center pixel and its neighbors. The authors shows that the LOGXoRP can extract effective texture (edge) features as compared to LBP and LGP. The performance of the proposed method is tested by conducting two experiments on Corel-5K and Corel-10K databases. The results of the proposed method after being investigated shows a significant improvement in terms of their evaluation measures as compared to LBP, LGP and other existing state-of-art techniques on respective databases.

References

[1]. Wei Jiang, Guihua Er, Qionghai Dai, and Jinwei Gu, (2006). "Similarity-Based Online Feature Selection in Content-Based Image Retrieval". IEEE Trans. Image Process, Vol.15, No.3, pp.702-712.

[2]. Ju Han and Kai-Kuang Ma, (2002). "Fuzzy Color Histogram and Its Use in Color Image Retrieval". IEEE Trans. Image Process, Vol.11, No.8, pp.944-952.

[3]. Aleksandra Mojsilovic, Jianying Hu, and Emina Soljanin, (2002). "Extraction of Perceptually Important Colors and Similarity Measurement for Image Matching, Retrieval, and Analysis". IEEE Trans. Image Process, Vol.11, No.11, pp.1238-1248.

[4]. Feng Jing, Mingjing Li, Hong-Jiang Zhang, and Bo Zhang, (2005). "A Unified Framework for Image Retrieval Using Keyword and Visual Features". IEEE Trans. Image Process, Vol.14, No.7, pp.979-989.

[5]. Smeulders A. W. M., Worring M., Santini S., Gupta A., and Jain R., (2000). "Content-Based Image Retrieval at the end of the Early Years". IEEE Trans. Pattern Anal. Mach. Intell, Vol.22, No.12, pp.1349-1380.

[6]. M. J. Swain, and D. H. Ballard, (1991). "Indexing via rd color histograms". Proc. 3 Int. Conf. Computer Vision, Rochester Univ., NY, pp.11-32.

[7]. J. Huang, S. R. Kumar,, and M. Mitra, (1997). "Combining supervised learning with color correlograms th for content-based image retrieval". Proc. 5 ACM Multimedia Conf., pp.325-334.

[8]. Ho Young Lee, Ho Keun Lee, and Yeong Ho Ha, (2003). "Spatial Color Descriptor for Image Retrieval and Video Segmentation". IEEE Trans. Multimedia, Vol.5, No.3, pp.358-367.

[9]. Ahmadian, and A. Mostafa, (2003). "An Efficient Texture Classification Algorithm using Gabor wavelet". Proc. EMBS, pp.930-933.

[10]. M. Unser, (1993). "Texture classification by wavelet packet signatures". IEEE Trans. Pattern Anal. Mach. Intell., Vol.15, No.11, pp.1186-1191.

[11]. P.W. Huang, and S.K. Dai, (2003). "Image retrieval by texture similarity". Pattern Recognition, Vol.36, pp.665- 679.

[12]. H. Abrishami Moghaddam, T. Taghizadeh Khajoie, A.H. Rouhi, and M. Saadatmand Tarzjan, (2005). "Wavelet correlogram: A new approach for image indexing and retrieval". Pattern Recognition, Vol.38, pp.2506-2518.

[13]. B. S. Manjunath, and W. Y. Ma, (1996). "Texture Features for Browsing and Retrieval of Image Data". IEEE Trans. Pattern Anal. Mach. Intell., Vol.18, No.8, pp.837- 842.

[14]. M. Kokare, P. K. Biswas, and B. N. Chatterji, (2007). "Texture image retrieval using rotated Wavelet Filters". Pattern Recognition Letters, Vol.28, pp.1240-1249.

[15]. M. Kokare, P. K. Biswas, and B. N. Chatterji, (2005). "Texture Image Retrieval Using New Rotated Complex Wavelet Filters". IEEE Trans. Syst., Man, Cybern. B, Cybern., Vol.33, No.6, pp.1168-1178.

[16]. M. Kokare, P. K. Biswas, and B. N. Chatterji, (2006). "Rotation-Invariant Texture Image Retrieval Using Rotated Complex Wavelet Filters". IEEE Trans. Syst., Man, Cybern. B, Cybern., Vol.36, No.6, pp.1273-1282.

[17]. Guang-Hai Liu, Lei Zhang, Ying-Kun Hou, Zuo-Yong Li, and Jing-Yu Yang, (2010). "Image retrieval based on multi-texton histogram". Pattern Recognition, Vol.43, pp.2380-2389.

[18]. Guang-Hai Liu, Zuo-Yong Li, Lei Zhang, and Yong Xu, (2011). "Image retrieval based on micro-structure descriptor". Pattern Recognition, Vol.44, pp.2123-2133.

[19]. Fariborz Mahmoudia, Jamshid Shanbehzadeh, Amir-Masoud Eftekhari-Moghadam, and Hamid Soltanian-Zadeh, (2003). "Image retrieval based on shape similarity by edge orientation autocorrelogram". Pattern Recognition, Vol.36, pp.1725-1736.

[20]. Heng Qi, Keqiu Li, Yanming Shen, and Wenyu Qu, (2010). "An effective solution for trademark image retrieval by combining shape description and feature matching". Pattern Recognition, Vol.43, pp.2017-2027.

[21]. T. Ojala, M. Pietikainen, and D. Harwood, (1996). "A comparative study of texture measures with classification based on feature distributions". Pattern Recognition, Vol.29, pp.51-59.

[22]. Loris Nanni, Sheryl Brahnam, and Alessandra Lumini, (2012). "A simple method for improving local binary patterns by considering non-uniform patterns". Pattern Recognition, Vol.45, pp.3844-3852.

[23]. XuemingQian, Xian-Sheng Hua, Ping Chen, and LiangjunKe, (2011). "PLBP: An effective local binary patterns texture descriptor with pyramid representation". Pattern Recognition, Vol.44, pp.2502-2515.

[24]. Z. Guo, L. Zhang, and D. Zhang, (2010). "Rotation invariant texture classification using LBP variance with global matchning". Pattern recognition, Vol.43, pp.706- 716.

[25]. Zhenhua Guo, Lei Zhang,, and David Zhang, (2010). "A Completed Modeling of Local Binary Pattern Operator for Texture Classification". IEEE Tans. Image Proc., Vol.19, No.6, pp.1657-1663.

[26]. YimoGuo, Guoying Zhao, and Matti Pietikainen, (2012). "Discriminative features for texture description". Pattern Recognition, Vol.45, pp.3834-3843.

[27]. Bongjin Jun, and Daijin Kim, (2012). "Robust face detection using local gradient patterns and evidence accumulation". Pattern Recognition, Vol.45, pp.3304- 3316.

[28]. ShufuXie, Shiguang Shan, and Xilin Chenand Jie Chen, (2010). "Fusing Local Patterns of Gabor Magnitude and Phase for Face Recognition". IEEE Trans. Image Process., Vol.19, No.5, pp.1349-1361.

[29]. Bongjin Jun, Taewan Kim, and Daijin Kim, (2011). "A compact local binary pattern using maximization of mutual information for face analysis". Pattern Recognition, Vol.44, pp.532-543.

[30]. SubrahmanyamMurala, R. P. Maheshwari, and R. Balasubramanian, (2012). "Local Maximum Edge Binary Patterns: A New Descriptor for Image Retrieval and Object Tracking". Signal Processing, Vol.92. pp.1467-1479.

[31]. SubrahmanyamMurala, R. P. Maheshwari, and R. Balasubramanian, (2012). "Directional Binary Wavelet Patterns for Biomedical Image Indexing and Retrieval". Journal of Medical Systems, Vol.36, No.5, pp.2865-2879.

[32]. SubrahmanyamMurala, R. P. Maheshwari, and R. Balasubramanian, (2012). "Local Tetra Patterns: A New Feature Descriptor for Content Based Image Retrieval". IEEE Trans. Image Process., Vol.21, No.5, pp.2874-2886.

[33]. SubrahmanyamMurala, R. P. Maheshwari, and R. Balasubramanian, (2012). "Directional local extrema patterns: a new descriptor for content based image retrieval". Int. J. Multimedia Information Retrieval, Vol.1, No.3, pp.191-203.

[34]. Valtteri Takala, Timo Ahonen, and Matti Pietikainen, (2005). "Block-Based Methods for Image Retrieval Using Local Binary Patterns". SCIA 2005, LNCS 3450, pp.882- 891.

[35]. Cheng-Hao Yao, and Shu-Yuan Chen, (2003). "Retrieval of translated, rotated and scaled color textures". Pattern Recognition, Vol.36, pp.913-929.

[36]. Loris Nanni, and Alessandra Lumini, (2008). "Local binary patterns for a hybrid fingerprint matcher". Pattern Recognition, Vol.41, pp.3461-3466.

[37]. Marko Heikkil, MattiPietikainen, and Cordelia Schmid, (2009). "Description of interest regions with local binary patterns". Pattern Recognition, Vol.42, pp.425- 436.

[38]. Image Rank Machine. Corel-10K image Database. Retrieved from http://www.ci.gxnu.edu.cn/cbir/ Dataset.aspx

Full Article (HTML)

Pdf

Research Paper

An Automated Fabric Fault Detection Using a Microcontroller

A. Selvarasi*

*PG Scholar, Department of Electronics and Communication Engineering, CK College of Engineering & Technology, Cuddalore, India.

Selvarasi, A. (2016). An Automated Fabric Fault Detection Using a Microcontroller. i-manager’s Journal on Pattern Recognition, 2(4), 11-17. https://doi.org/10.26634/jpr.2.4.5944

Abstract

Textile industries are one of the major revenue generating industries in Tamil Nadu, India. Greater efforts are taken in manufacturing the good quality fabrics. Defects in a fabric are a major issue to the textile industry. Textile industries in Tamil Nadu initially had only manual inspection strategy for the detection of faults. Later, automation has been made through image processing techniques. Traditional inspection process for fabric defects is by human visual inspection, which is inefficient, costly and time consuming. To enhance the accuracy of fabric defects detection, and help people out from this tedious and stressful work, an automated fabric inspection system has been proposed. To automate this process, the fault present on the fabrics can be identified using MATLAB with Image processing techniques and the implementation of this idea is done in Arduino kit for real time applications.

References

[1]. Dorrity J., Vachtsevanos G. and Jasper W. (1995). “Real-time fabric defect detection and control in weaving processes”. National Textile, pp.143-152.

[2]. Kumar A. (2008). “Computer vision-based fabric defect detection: A survey”. IEEE, Transactions on Industrial Electronics, Vol.55, No.1, pp.348-363.

[3]. Mahajan P.M., Kolhe S.R. and Pati P.M. (2009). “A review of automatic fabric defect detection techniques”. Advances in Computational Research, ISSN: 0975–3273, Vol.1, No.2, pp.18-29.

[4]. Mitropoulos P., Koulamas C., Stojanovic R., Koubias S., Papadopoulos G., and Karagiannis G. (1999). “A realtime vision system for defect detection and neural classification of web textile fabric”. Electronic Imaging '99 International Conference.

[5]. Malamas E.N., Petrakis E.G.M., Zervakis M., Petit L. and Legat J.D. (2003). “A survey on industrial vision systems, applications and tools”. Image and Vision Computing, Vol.21, pp.171.

[6]. Priyanka Vyas, Manish Kakhani, (2015). “Fabric Fault Processing Using Image Processing Techniques”. International Journal of Multidisciplinary Research and Development, Vol.2, No.2, pp.29-31.

[7]. Jagruti mahure & Y.C. Kulkarni, (2013). “Fabric Faults Processing: Perfections And Imperfections”. International Journal of Computer Science and Technology (IJCST) ISSN : 0976-8491 (Online), ISSN : 2229-4333 (Print), Vol.4, No.2.

[8]. G.M. Nasira, P. Banumathi, (2013). “Plain Woven Fabric Defect Detection Based on Image Processing and Artificial Neural Networks”. International Journal of Computer Trends and Technology (IJCTT), Vol.6, No.4.

[9]. R.S. Sabeenian, M.E. Paramasivam, and P.M. Dinesh, (2011). “Detection And Location Of Defects In Handloom Cottage Silk Fabrics Using MRMRFM & MRCSF”. IJTE, Vol.2, pp.1-21.

[10]. H. Ibrahim, L. Canan, Mehmet. (2013). “Developing An Algorithm For Defect Detection of Denim Fabric: Gabor Filter Method”. Tekstil Ve Konfeksiyon, Vol.23, No.2.

[11]. J. Rajalakshmi and A. Selvarasi, (2015). “A Hybridized Algorithm for Fabric Fault Detection”. International Journal of Research in Engineering.

Full Article (HTML)

Pdf

Research Paper

Improved Tree Leaves Segmentation Using Hybrid GAC Approach

Nivasini.R.P* , S. Thilagamani**

* PG Scholar, Department of Computer Science and Engineering, M. Kumarasamy College of Engineering (Autonomous), Karur, India.

** Dean, Department of Computer Science and Engineering, M. Kumarasamy College of Engineering (Autonomous), Karur, India.

Nivasini, R. P., and Thilagamani, S. (2016). Improved Tree Leaves Segmentation Using Hybrid GAC Approach. i-manager’s Journal on Pattern Recognition, 2(4), 18-25. https://doi.org/10.26634/jpr.2.4.5945

Abstract

Leaves are one of the important parts in a tree. Extracting accurately the shape of a leaf is a crucial step in image-based systems. The partial or total absence of textures on leaf surface and the high color variability of leaves belonging to the same species make shape as the main recognition element. For such reasons, leaf segmentation plays a decisive role in the leaf extraction process. Even though many general segmentation methods have been proposed in the last decades, leaf segmentation presents specific challenges. In particular, a pixel-level precision is required in order to highlight fine scale boundary structures and discriminate similar global shapes. The authors propose a robust and accurate method for segmenting objects acquired under various controlled conditions. Then, they have improved the performance of the segmentation methods using preprocessing tools such as, color distance map and input strokes. Based on these methods, we can eliminate unwanted boundaries and localize the leaf object efficiently. They have implemented a Hybrid Guided Active Contour (GAC) method to measure geometric properties of leaf images, and have provided with a comparative study for various segmentation algorithms based on performance metrics. Based on experimental results, GAC provides improved performance in leaf datasets.

References

[1]. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, (2012). “SLIC superpixels compared to state-ofthe- art superpixel methods”. IEEE Trans. Pattern Anal. Mach. Intell., Vol.34, No.11, pp.2274-2282.

[2]. P. Brigger, J. Hoeg, and M. Unser, (2000). “B-spline snakes: A flexible tool for parametric contour detection”. IEEE Trans. Image Process., Vol.9, No.9, pp.1484-1496.

[3]. J. Canny, (1986). “A computational approach to edge detection”. IEEE Trans. Pattern Anal. Mach. Intell., Vol.8, No.6, pp.679-698.

[4]. D. Casanova, J. B. Florindo, W. N. Gonçalves, and O. M. Bruno, (2012). “IFSC/USP at ImageCLEF 2012: Plant identification task”. Proc. Eval. Labs Workshop, Online Working Notes, pp.1-7.

[5]. G. Cerutti, L. Tougne, J. Mille, A. Vacavant, and D. Coquin, (2013). “Understanding leaves in natural images-A model-based approach for tree species identification”. Comput. Vis. Image Understand., Vol.117, No.10, pp.1482-1501.

[6]. T. F. Chan and L. A. Vese, (2001). “Active contours without edges”. IEEE Trans. Image Process., Vol.10, No.2, pp.266-277.

[7]. Y. Cheng, (1995). “Mean shift, mode seeking, and clustering”. IEEE Trans. Pattern Anal. Mach. Intell., Vol.17, No.8, pp.790-799.

[8]. D. Comaniciu and P. Meer, (2002). “Mean shift: A robust approach toward feature space analysis”. IEEE Trans. Pattern Anal. Mach. Intell., Vol.24, No.5, pp.603- 619.

[9]. C. Couprie, L. Najman, L. Grady, and H. Talbot, (2009). “Power watersheds: A new image segmentation framework extending graph cuts, randomwalker and th optimal spanning forest”. Proc. IEEE 12 Int. Conf. Comput. Vis., pp.731-738.

[10]. P. F. Felzenszwalb and D. P. Huttenlocher, (2004). “Efficient graph-based image segmentation”. Int. J. Comput. Vis., Vol.59, No.2, pp.167-181.

[11]. H. Goëau et al., (2011). “The CLEF 2011 plant images classification task”. Image CLEF 2011 Working Notes.

[12]. J. Horvath, (2006). “Image segmentation using fuzzy c-means”. Proc. Symp. Appl. Mach. Intell., pp.144-151.

[13]. J. Shi and J. Malik, (2000). “Normalized cuts and image segmentation”. IEEE Trans. Pattern Anal. Mach. Intell., Vol.22, No.8, pp.888-905.

[14]. A. Karsnas, R. Strand, and P. K. Saha, (2012). “The st vectorial minimum barrier distance”. Proc. 21 Int. Conf. Pattern Recognit., pp.792-795.

[15]. M. Kass, A. Witkin, and D. Terzopoulos, (1988). “Snakes: Active contour models”. Int. J. Comput. Vis., Vol.1, No.4, pp.321-331.

[16]. A. M. Khan and S. Ravi, (2013). “Image segmentation methods: A comparative study”. Int. J. Soft Comput. Eng., Vol.3, No.4, pp.84-92.

[17]. N. Kumar et al., (2012). “Leafsnap: A computer vision system for automatic plant species identification”. Proc. Eur. Conf. Comput. Vis., pp.502-516.

[18]. M. Lynch, O. Ghita, and P. F. Whelan, (2006). “Automatic segmentation of the left ventricle cavity and myocardium in MRI data”. Comput. Biol. Med., Vol.36, No.4, pp.389-407.

[19]. J. Weber, S. Lefevre, and P. Gancarski, (2011). “Interactive video segmentation based on quasi-flat zones”. Proc. 7 Int. Symp. Image Signal Process.Anal., pp.265-270.

[20]. N. Valliammal and S. N. Geethalakshmi, (2012). “Plant leaf segmentation using non linear K means clustering”. Int. J. Comput. Sci. Issues, Vol.9, No.3, pp.212- 218.

Full Article (HTML)

Pdf

Review Paper

An Analytical Study of Handwritten Character Recognition

Ujwal Singh Vohra* , Shriprakash Dwivedi, Hardwari Lal Mandoria*

* PG Scholar, Department of Information Technology, G.B. Pant University of Agriculture & Technology, Pantnagar, India.

Assistant Professor, Department of Information Technology, G.B. Pant University of Agriculture & Technology, Pantnagar, India.

* Professor & Head, Department of Information Technology, G.B. Pant University of Agriculture & Technology, Pantnagar, India.

Vohra, U. S., Dwivedi, S. P., and Mandoria, H. L. (2016). An Analytical Study of Handwritten Character Recognition. i-manager’s Journal on Pattern Recognition, 2(4), 26-41. https://doi.org/10.26634/jpr.2.4.5946

Abstract

Handwritten Character Recognition is a crucial part of Optical Character Recognition (OCR) through which the computer understands the handwriting of individuals automatically from the image of a handwritten script. From a decade, OCR becomes the most important application of Pattern Recognition, Machine Vision and Signal Processing for the rapid growth of technology, which can be described as the Electronic or Mechanical conversion of the captured or scanned image. The image is converted into the machine encoded form that can be further used in machine translation, text to speech conversion, text mining and the storage of data. Selections of appropriate feature extraction and classification methods are the crucial factors for achieving a higher rate of recognition with greater level of accuracy for handwritten characters to accurately achieve recognition of each and every letter. Here, in this paper the authors attempt to give a more elaborative image for a comprehensive review that has been proposed to achieve a deep study of the handwritten characters recognition, and this data will be useful for the readers working in the field of handwritten character recognition.

References

[1]. A. Kundu, and Y. He, (1991). “On optimal order in modeling sequence of letters in words of common language as a Markov chain”. Pattern Recognit., Vol.24, No.7, pp.603-608.

[2]. Anoop M. Namboodiri, and Anil K. Jain, (2004). “Online Handwritten Script Recognition”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.26, No.1.

[3]. Anil K. Jain, Robert P.W. Duin, and Jianchang Mao, (2000). “Statistical Pattern Recognition: A Review”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.22, No.1, pp.4-37.

[4]. Arjun Singh, and Kansham Angphun Maring, (2015). “Handwritten Devanagari Character Recognition using SVM and ANN”. International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, No. 8, pp. 123-128.

[5]. Anshul Gupta, Manisha Srivastava, and Chitralekha Mahanta, (2011). “Offline Handwritten Character Recognition Using Neural Network”. IEEE (ICCAIE), pp. 102- 107.

[6]. B. Hari Kumar, Y.E. Vasanth Kumar and Mohammad Shaffi, (2014). “Optical character recognition using Split Profile Algorithm”. International Journal of Advanced Engineering and Global Technology, Vol.2, No.12.

[7]. Brijmohan Singh, Ankush Mittal, M.A. Ansari and Debashis Ghosh, (2011). “Handwritten Devanagari Word Recognition: A Curvelet Transform Based Approach”. IJCSE, Vol.3, No. 4, pp. 1658- 1665.

[8]. Chanchal Bansal, and Arif Khan, (2014). “Handwritten Numeral Recognition using SVM and Chain Code”. IJARET, Vol.2, No.7.

[9]. C.Y. Suen, M. Berthod, and S. Mori, (1980). “Automatic recognition of handprinted characters—The state of the art”. Proc. IEEE, Vol.68, pp.469-487.

[10]. C. V. Jawahar, M. N. S. S. K. Pavan Kumar and S. S. Ravi Kiran, (2003). “A Bilingual OCR for Hindi-Telugu Documents and its Applications”. Seventh International Conference on Document Analysis and Recognition (ICDAR).

[11]. Deepa Berchmans, and S.S. Kumar, (2014). “Optical Character Recognition: An Overview and an Insight”. IEEE, International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp. 1361-1365.

[12]. Divakar Yadav, Sonia Sánchez-Cuadrado and Jorge Morato, (2013). “Optical Character Recognition for Hindi Language Using a Neural-network Approach”. Journal of Information Processing Systems, Vol.9, No.1, pp.117-140.

[13]. Gaurav Y. Tawde, (2014). “Optical Character Recognition for Isolated Offline Handwritten Devanagari Numerals Using Wavelets”. Journal of Engineering Research and Applications, Vol.4, No.2.

[14]. H. Cheng, W. H. Hsu, and M. C. Kuo, (1993). “Recognition of handprinted Chinese characters via stroke relaxation”. Pattern Recognit., Vol.26, No.4, pp.579-593.

[15]. I.S. Oh and C. Y. Suen, (1998). “Distance features for neural network-based recognition of handwritten characters”. Int. J. Document Anal.Recognit., Vol.1, No.2, pp.73-88.

[16]. J. Rocha and T. Pavlidis, (1994). “A shape analysis model”. IEEE Trans. Pattern Anal. Machine Intell., Vol.16, pp.394-404.

[17]. J. Kitler, (1986). “Feature selection and extraction”. In Handbook of Pattern Recognition and Image Processing. T. Young and K. Fu, Eds, NewYork: Academic, pp.59-83.

[18]. J. Hu, and T. Pavlidis, (1996). “A Hierarchical Approach to Efficient Curvilinear Object Searching”. Computer Vision and Image Understanding, Vol.63, No.2, pp.208-220.

[19]. K. Y. Rajput and Sangeeta Mishra, “Recognition and Editing of Devnagari Handwriting Using Neural Network”. SPIT-IEEE Colloquium and Intl. Conference, Mumbai, India.

[20]. K. Chung and J. Yoon, (1997). “Performance comparison of several feature selection methods based on node pruning in handwritten character recognition”. In Proc. 4 Int. Conf. Document Anal. Recognit., Ulm, Germany, pp.11-15.

[21]. K. Wang, Y.Y. Tang, and C.Y. Suen, (1988). “Multilayer projections for the classification of similar Chinese characters”. In Proc. 9 Int. Conf. Pattern Recognit., pp.842-844.

[22]. K.T. Miura, R. Sato, and S. Mori, (1997). “A method of extracting curvature features and its application to handwritten character recognition”. In Proc. 4 Int. Conf. Document Anal. Recognit., Ulm, Germany, pp.450-454.

[23]. M.D. Garris, et al., (1994). “NIST form based handprinted recognition system”. Nat. Inst. Standard Technol., Gaithersburg, MD, Tech. Rep. NISTIR 546 920 899.

[24]. M. Egmont-Petersen, and D. De Ridder, H. Handels, (2002). “Image Processing with Neural Networks: A Review”. Pattern Recognition, Vol.35, pp.2279-2301.

[25]. M. Hamanaka, K. Yamada, and J. Tsukumo, (1993). “On-Line Japanese Character Recognition Experiments by an Off-Line Method Based on Normalization cooperated Feature Extraction”. In Proc. 3 ICDAR, pp.204- 207.

[26]. M.K. Brown and S. Ganapathy, (1983). “Preprocessing techniques for cursive script word recognition”. Pattern Recognit., Vol.16, No.5, pp.447-458.

[27]. Munish Kumar, M.K. Jindal and R. K. Sharma, (2011). “k -Nearest Neighbor Based Offline Handwritten Gurmukhi Character Recognition”. IEEE ICIIP.

[28]. Mohamed Cheriet, Nawwaf Kharma, and Cheng- Lin Liu, Ching Y. Suen, (2007). Character Recognition Systems. A Guide for Students and Practioners. John Wiley & Sons, Inc.

[29]. Malakar, Samir, et al., (2012). “Text line extraction from handwritten document pages using spiral run length smearing algorithm”. Communications, Devices and Intelligent Systems (CODIS), International Conference on, IEEE.

[30]. M N S S K Pavan Kumar, S S Ravikiran, Abhishek Nayani, C V Jawahar and P J Narayanan, (2003). “Tools for Developing OCRs for Indian Scripts”.Computer Vision and Pattern Recognition Workshop, 2003, CVPRW’03, Conf. on, IEEE, Vol. 3, pp. 33.

[31]. Monica Patel, and Shital P. Thakkar, (2015). “Handwritten Character Recognition in English: A Survey”. International Journal of Advanced Research in Computer and Communication Engineering, Vol.4, No.2.

[32]. Mohammad Abu Obaida, Md. Jakir Hossain, Momotaz Begum and Md. Shahin Alam, (2011). “Multilingual OCR (MOCR): An Approach to Classify Words to Languages”. International Journal of Computer Applications, Vol.32, No.1.

[33]. Nabin. Sharma, U. Pal, F. Kimura, and S. Pal, (2006). “Recognition of Off-Line Handwritten Devnagari Characters Using Quadratic Classifier”. Springer-Verlag, Berlin Heidelberg ICVGIP, LNCS 4338, pp.805-816.

[34]. Nafiz Arica, and Fatos T. Yarman-Vural, (2001). “An Overview of Character Recognition Focused on Off-Line Handwriting”. IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, Vol.31, No.2.

[35]. N. Otsu, (1979). “A threshold selection method from gray-level histograms”. IEEE Transactions on Systems, Man, and Cybernetics SMC, Vol.9(1), pp.62-66.

[36]. N. Arica and F.T. Yarman-Vural, (2000). “One dimensional representation of two dimensional information for HMM based handwritten recognition”. Pattern Recognit. Lett., Vol.21, No.6-7, pp.583-592.

[37]. Nafiz Arica, (1998). “An Offline Character Recognition System for Free Style Handwriting”. (Doctorial Dissertation), The Middle East Technical University.

[38]. Ohhira T., Pecharanin N., Taguchi A., Iijima, N., Akima, Y., and Sone, M., (1995). “Chinese character recognition by the auto recognition system”. IEEE International Conference on Neural Networks, Vol.5, pp.2222-2225.

[39]. Paul W. Handel, (1993). Statistical Machine. U.S. Patent 1915993, Retrieved From http://www.google.com/ patents? vid=USPAT1915993.

[40]. P.S. Deshpande Latesh Malik, and Sandhya Arora, (2007). “Recognition of Handwritten Devanagari Characters with Percentage Component Regular Expression Matching and Classification Tree”. TENCON 2007-2007, IEEE Region 10 Conference, pp.1-4.

[41]. P.D. Gader and M. A. Khabou, (1996). “Automatic feature generation for handwritten digit recognition”. IEEE Trans. Pattern Anal. Machine Intell., Vol.18, pp.1256-1262.

[42]. R.J. Ramteke, and S.C. Mehrotra, (2008). “Recognition of Handwritten Devnagari Numerals”. International Journal of Computer Processing of Oriental Languages.

[43]. Rekha Singh, (2014). “Character Recognition for Bi- Lingual Mixed-type Characters using Artificial Neural Network”. IJRET: International Journal of Research in Engineering and Technology, Vol.3, Special Issue.10.

[44]. S.S. Wang, P.C. Chen, and W.G. Lin, (1994). “Invariant pattern recognition by moment Fourier descriptor”. Pattern Recognit., Vol.27, pp.1735-1742.

[45]. S. Madhvanath and V. Govindaraju, (1999). “Reference lines for holistic recognition of handwritten words”. Pattern Recognit., Vol.32, No.12, pp.2021-2028.

[46]. S. Madhvanath, G. Kim, and V. Govindaraju, (1999). “Chaincode contour processing for handwritten word recognition”. IEEE Pattern. Anal. Machine Intell., Vol.21, pp.928-932.

[47]. S. Madhvanath, E. Kleinberg, V. Govindaraju, and S. N. Srihari, (1997). “The HOVER system for rapid holistic verification of off-line handwritten phrases”. In Proc. 4 Int. Conf. Document Anal. Recognit., Ulm, Germany, pp.855- 890.

[48]. S.W. Lee and Y.J. Kim, (1995). “Direct extraction of topographic features for gray-scale character recognition”. IEEE Trans. Pattern Anal. Machine Intell., Vol.17, pp.724-729.

[49]. Sandhya Arora, et al., (2010). “Performance Comparison of SVM and ANN for Handwritten Devanagari Character Recognition”. IJCSI International Journal of Computer Science Issues, Vol.7, No.3.

[50]. Sandhya Arora, Debotosh Bhattacharjee, Mita Nasipuri, D. K. Basu and M. Kundu, (n.d.). “ Recognition of Non-Compound Handwritten Devanagari Characters using a Combination of MLP and Minimum Edit Distance”. International Journal of Computer Science and Security (IJCSS), Vol.4, No.1, pp.107-120.

[51]. Shailedra Kumar Shrivastava, and Sanjay S. Gharde, (2010). “Support Vector Machine for Handwritten Devanagari Numeral Recognition”. International Journal of Computer Applications, Vol.7, No.11.

[52]. Sung-Bae Cho, (2002). Fusion of Neural Networks with Fuzzy Logic and Genetic Algorithm. IOS Press, pp.363-372.

[53]. Sanjay Chandra Arya, Rajesh Shyam Singh and Hardwari Lal Mandoria, (2015). “Image Denoising in Hand Written Document for Degraded Documents using Wiener Filter Algorithm”. International Journal for Research in Emerging Science and Technology, Vol.2, No.7.

[54]. Satish Kumar, (2009). “Per formance and Comparison of Features on Devanagari Hand-printed Dataset,” Inlt. Journal of Recent Trends in Engineering, Vol.1, No.2.

[55]. Sandhya Arora, Debotosh Bhattacharjee, Mita Nasipuri, M. Kundu and D. K. Basu and L. Malik, (2010). “Handwritten Devanagari Numeral Recognition using SVM & ANN”. IJCSE40, Vol.1, No.2.

[56]. I.K. Sethi and B. Chatterjee, (1976). “Machine Recognition of hand printed Devanagri numerals”. J. Inst. Electron Telecommun, Engg, pp. 532-535.

[57]. Saurabh Farkya, Govinda Surampudi, and Ashwin Kothari, (2015). “Hindi Speech Synthesis by concatenation of recognized Hand written Devanagri Script using Support Vector Machines Classifier”. IEEE ICCSP Conference.

[58]. R.M.K. Sinha, and H.N. Mahabala, (1979). “Machine Recognition of Devanagri Script. IEEE Trans. System. Man Cybern, pp. 435-441.

[59]. Tapan K Bhowmik, Swapan K Parui Utpal Roy, (2008). “Discriminative HMM Training with GA for Handwritten Word Recognition”. IEEE.

[60]. Teena Mittal, and Rajendra Kumar Sharma, (2015). “Multiclass SVM based Spoken Hindi Numerals Recognition”. International Arab Journal of Information Technology, Vol.12, No.6A.

[61]. Umapada Pal, Sukalpa Chanda Tetsushi, Wakabayashi, and Fumitaka Kimura, (n.d.). “Accuracy Improvement of Devnagari Character Recognition Combining SVM and MQDF”.

[62]. U. Pal, T. Wakabayashi, and F. Kimura, (2009). “Comparative Study of Devnagari Handwritten Character Recognition using Different Feature and Classifiers”. 10 Intl. Conf. on Document Analysis and Recognition, pp.1111-1115.

[63]. Vikas J Dongre, and Vijay H Mankar, (2010). “A Review of Research on Devnagari Character Recognition”. International Journal of Computer Applications, Vol.12, No.2, pp.0975- 8887.

[64]. Vijay Laxmi Sahu, and Babita Kubde, (2013). “Offline Handwritten Character Recognition Techniques using Neural Network: A Review”. International Journal of Science and Research (IJSR), Vol.2, Issue.1.

[65]. W. Lu, Y. Ren, and C. Y. Suen, (1991). “Hierarchical attributed graph representation and recognition of handwritten chinese characters”. Pattern Recognit., Vol.24, No.7, pp.617-632.

[66]. W.Y. Kim and P. Yuan, (1994). “A practical pattern recognition system for translation, scale and rotation invariance”. In Proc. Int. Conf. Comput. Vis.Pattern Recognit., Seattle, WA.

[67]. X. Li, W. Oh, J. Hong, and W. Gao, (1997). “Recognizing components of handwritten characters by attributed relational graphs with stable features”. In Proc. 4^th Int. Conf. Document Anal. Recognit., pp.616-620.

[68]. X. Zhu, Y. Shi, and S. Wang, (1999). “A new algorithm of connected character image based on Fourier transform”. In Proc. 5^th Int. Conf. DocumentAnal. Recognit., Bangalore, India, pp.788-791.

[69]. Y. Tao and Y. Y. Tang, (1999). “The feature extraction of Chinese character based on contour information”. In Proc. 5^th Int. Conf. Document Anal.Recognit., Bangalore, India, pp.637-640.

[70]. Y. C. Chim, A. A. Kassim, and Y. Ibrahim, (1999). “Character recognition using statistical moments”. Image Vis. Comput., Vol.17, pp.299-307.

[71]. D. Trier, A.K. Jain, and T. Taxt, (1996). “Feature extraction method for character recognition—A survey”. Pattern Recognit., Vol.29, No.4, pp.641-662.

[72]. (1995). “Multiresolutional recognition of handwritten numerals with wavelet transform and multilayer cluster rd neural network”. In Proc. 3^rd Int. Conf. Document Anal. Recognit., Montreal, QC, Canada, pp.1010-1014.

[73]. D. Guillevic and C.Y. Suen,(1998). “Recognition of legal amounts on bank cheques”. Pattern Anal. Applicat., Vol.1, No.1, pp.28- 41.

[74]. (1998). “HMM–KNN word recognition engine for bank cheque processing”. In Proc. 14^th Int. Conf. Pattern Recognit., Brisbane, Australia, pp.1526-1529.

Full Article (HTML)

Pdf

Review Paper

A Comprehensive Study on Different Pattern Recognition Techniques

Navneet Kr. Kashyap* , B.K.Pandey, Hardwari Lal Mandoria*, Ashok Kumar****

* PG Scholar, Department of Information Technology, G.B.P.U.A & T, Pantnagar, India.

, Assistant Professor, Department of Information Technology, G.B.P.U.A & T, Pantnagar, India.

* Professor and Head, Department of Information Technology, G.B.P.U.A & T, Pantnagar, India.

Kashyap, N. K., Pandey, B. K., Mandoria, H. L., and Kumar, A. (2016). A Comprehensive Study on Different Pattern Recognition Techniques. i-manager’s Journal on Pattern Recognition, 2(4), 42-49. https://doi.org/10.26634/jpr.2.4.5947

Abstract

Pattern Acceptance has been admired because of its advancement in the appliance areas. The applying breadth includes medicine, communications, automation, aggressive intelligence, abstracts mining, bioinformatics, certificate classification, accent recognition, business and abounding others. In this analysis, cardboard assorted approaches of Arrangement Acceptance has been presented with their pros-cons, and the appliance specific archetype has been confirmed. From the base of the survey, arrangement acceptance techniques could be categorized into six parts. Such awning techniques include Neural Network scheme, Statistics Techniques, Template Matching, Hybrid versions and Fuzzy Model.

References

[1]. H.M. Abbas and M.M. Fahmy, (1994). “Neural Networks for Maximum Likelihood Clustering”. Signal Processing, Vol.36, No.1, pp.111-126.

[2]. H. Akaike, (1974). “A New Look at Statistical Model Identification”. IEEE Trans. Automatic Control, Vol.19, pp.716-723.

[3]. S. Amari, T.P. Chen, and A. Cichocki, (1997). “Stability Analysis of Learning Algorithms for Blind Source Separation”. Neural Networks, Vol.10, No.8, pp.1,345- 1,351.

[4]. A. Antos, L. Devroye, and L. Gyorfi, (1999). “Lower Bounds for Bayes Error Estimation”. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.21, No.7, pp.643- 645.

[6]. C.M. Bishop, (1995). Neural Networks for Pattern Recognition. Oxford: Clarendon Press.

[7]. C.J.C. Burges, (1998). “A Tutorial on Support Vector Machines for Pattern Recognition”. Data Mining and Knowledge Discovery, Vol.2, No.2, pp.121-167.

[8]. B. Cheng and D.M. Titterington, (1994). “Neural Networks: A Review from Statistical Perspective”. Statistical Science, Vol.9, No.1, pp.2-54.

[9]. P.A. Chou, (1991). “Optimal Partitioning for Classification and Regression Trees”. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.13, No.4, pp.340- 354.

[10]. T.M. Cover, (1992). “Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition”. IEEE Trans. Electronic Computers, Vol.14, pp.326-334.

[11]. P.A. Devijver and J. Kittler, (1989). Pattern Recognition: A Statistical Approach. London: Prentice Hall.

[12]. L. Devroye, (1988). “Automatic Pattern Recognition: A Study of the Probability of Error”. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.10, No.4, pp.530- 543.

[13]. U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, (1999). “Knowledge Discovery and Data Mining: Towards a Unifying Framework”. Proc. Second Int'l Conf. Knowledge Discovery and Data Mining.

[14]. Anil K Jain, Robert P.W. Duin and Jianchang Mao, (2000). “Statistical Pattern Recognition: A review”. IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.22, No.1, pp.4-37.

[15]. Scott C Newton, Surya Pemmaraju and Sunanda Mitra, (1992). “Adaptive Fuzzy Leader Clustering of Complex Data Sets in Pattern recognition”. IEEE Transactions on Neural Network, Vol.3, No.5, pp.794-800.

[16]. Jenn- Hwai Yang and Miin Shen Yang, (2005). “A Control Chart Pattern Recognition System Using Statistical Correlation Coefficient Method”. Computers and Industrial Engineering, Vol.48, pp.205-221.

[17]. Jun Hai Zhai, (2006). “An Overview of Pattern th Classification Methodologies”. Proceedings of 5 International Conference on Machine Learning and Cybernetics, pp.3222-3227.

[18]. Liu, J., Sun, J. And Wang, S. (2006). “Pattern Recognition: An overview”. International Journal of Computer Science and Network Security (IJCSNS), Vol.6, No.6.

[19]. Vapnik, V., (2010). The Nature of Statistical Learning Theory. Springer.

[20]. Robert Jain, and Mao, (2000). “Statistical pattern recognition: A Review”. IEEE Transaction on Machine Learning and Pattern Analysis, Vol.22, No.1.

[21]. Wikipedia. Introduction to pattern recognition.

[22]. Thomas Minka. A Statistical Learning/Pattern Recognition.

[23]. Zheng, He, (2011). Classification Technique in Pattern Recognition, University of Technology, Sydney, ISBN 80-903100-8-7, WSCG.

[24]. Seema Asht, and Rajeshwar Dass, (2012). “Pattern Recognition Techniques: A Review”. International Journal of Computer Science and Telecommunications.

[25]. M. Parasher, S. Sharma, A.K Sharma, and J.P Gupta, (2011). “Anatomy on Pattern Recognition”. Indian Journal of Computer Science and Engineering (IJCSE), Vol.2, No.3.

[26]. Amin Fazel and Shantnu Chakrabartty, (1996). “An Overview of Statistical Pattern Recognition Techniques for Speaker Verification”. IEEE Circuits and Systems, pp.61-81.

[27]. K.S. Fu, (1982). Syntactic Pattern Recognition and Applications. Englewood Cliffs, N.J.: Prentice-Hall.

[28]. K. Tumer and J. Ghosh, (1996). "Analysis of Decision Boundaries in Linearly Combined Neural Classifiers". Pattern Recognition, Vol.29, pp.341-348.

[29]. R.O.Duda and P.E.Hart, (1973). Pattern Classification and Scene, Analysis, New York: John Wiley & sons.

[30]. Majida Ali Abed, Ahmad Nasser Ismail and ZubadiMatiz Hazi, (June, 2010). “Pattern recognition Using Genetic Algorithm”. International Journal of Computer and Electrical Engineering, Vol.2, No.3.

[31]. Mohammad S. Alam, and Mohammad A. Karim, (2004). “Advances in Pattern Recognition Algorithms, Architectures and Devices”. Optical Engineering, Vol.43, No.8.

[32]. B. Ripley, (1993). Statistical Aspects of Neural Networks, Networks on Chaos: Statistical and Probabilistic Aspects. U. Bornndorff-Nielsen, J.Jensen, and W. Kendal, Eds., Chapman and Hall.

[33]. J. Anderson, A. Pellionisz, and E. Rosenfeld, (1990). Neuro Computing 2: Directions for Research. Cambridge Mass.: MIT Press.

[34]. T. Pavlidis and F. Ali, (1975). "Computer Recognition of Handwritten Numerals by Polygonal Approximations”. IEEE Trans. Syst.,Man,Cybern. Vol. SMC-5.

[35]. Anupam Joshi, Narendram Ramakrishman, Elias N. Houstis and John R. Rice, (1997). "On Neurobiological, Neuro-Fuzzy, Machine Learning And Statistical Pattern Recognition Techniques". IEEE Trans. on Neural Networks, Vol.8, No.1.

[36]. J.C. Bezdek, (1981). Pattern Recognition with Fuzzy Objective Function Algorithm, New York: Plenum Press.

[37]. R. O. Duda, P. E. Hart and D. G. Stork, (2000). Pattern Classification, John Wiley & Sons.

[38]. B. Ripley, (1996). Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge.

[39]. Sergios Theodoridis, and Konstantinos Koutroumbas, (1982). “Pattern recognition”. Pattern Recognition, Elsevier, USA.

[40]. Anil k Jain, and Robert P.W Duin, (2004). Introduction To Pattern Recognition. The Oxford Companion to the Mind, Second Edition, Oxford University Press, Oxford, UK, pp.698- 703.

[41]. S.P. Shinde and V.P. Deshmukh, (2011). “Implementation of Pattern Recognition Techniques and Overview of its Applications in various areas of Artificial Intelligence”. International Journal of Advances in Engineering & Technology, Vol.1, No.4, pp.127-137.

i-manager's Journal on Pattern Recognition (JPR)

Current Issue Vol. 10 Issue 2

Most Read

Most Cited

Volume 2 Issue 4 December - February 2016 [Open Access]

Local Orientation Gradient XOR Patterns: A New Feature Descriptor for Image Indexing and Retrieval

A. Hariprasad Reddy* , N. Subhash Chandra**

* Research Scholar, Department of Computer Science and Engineering, JNTU Hyderabad, Andhra Pradesh, India. ** Principal & Professor, Holy Mary Institute of Technology and Science, Hyderabad, Andhra Pradesh, India.

Reddy, A. H., and Chandra, N. S. (2016). Local Orientation Gradient XOR Patterns: A New Feature Descriptor for Image Indexing and Retrieval. i-manager’s Journal on Pattern Recognition, 2(4), 1-10. https://doi.org/10.26634/jpr.2.4.5943

Abstract

References

Full Article (HTML)

Pdf

An Automated Fabric Fault Detection Using a Microcontroller

A. Selvarasi*

*PG Scholar, Department of Electronics and Communication Engineering, CK College of Engineering & Technology, Cuddalore, India.

Selvarasi, A. (2016). An Automated Fabric Fault Detection Using a Microcontroller. i-manager’s Journal on Pattern Recognition, 2(4), 11-17. https://doi.org/10.26634/jpr.2.4.5944

Abstract

References

Full Article (HTML)

Pdf

Improved Tree Leaves Segmentation Using Hybrid GAC Approach

Nivasini.R.P* , S. Thilagamani**

* PG Scholar, Department of Computer Science and Engineering, M. Kumarasamy College of Engineering (Autonomous), Karur, India. ** Dean, Department of Computer Science and Engineering, M. Kumarasamy College of Engineering (Autonomous), Karur, India.

Nivasini, R. P., and Thilagamani, S. (2016). Improved Tree Leaves Segmentation Using Hybrid GAC Approach. i-manager’s Journal on Pattern Recognition, 2(4), 18-25. https://doi.org/10.26634/jpr.2.4.5945

Abstract

References

Full Article (HTML)

Pdf

An Analytical Study of Handwritten Character Recognition

Ujwal Singh Vohra* , Shriprakash Dwivedi**, Hardwari Lal Mandoria***

Vohra, U. S., Dwivedi, S. P., and Mandoria, H. L. (2016). An Analytical Study of Handwritten Character Recognition. i-manager’s Journal on Pattern Recognition, 2(4), 26-41. https://doi.org/10.26634/jpr.2.4.5946

Abstract

References

Full Article (HTML)

Pdf

A Comprehensive Study on Different Pattern Recognition Techniques

Navneet Kr. Kashyap* , B.K.Pandey**, Hardwari Lal Mandoria***, Ashok Kumar****

* PG Scholar, Department of Information Technology, G.B.P.U.A & T, Pantnagar, India. **,**** Assistant Professor, Department of Information Technology, G.B.P.U.A & T, Pantnagar, India. *** Professor and Head, Department of Information Technology, G.B.P.U.A & T, Pantnagar, India.

Kashyap, N. K., Pandey, B. K., Mandoria, H. L., and Kumar, A. (2016). A Comprehensive Study on Different Pattern Recognition Techniques. i-manager’s Journal on Pattern Recognition, 2(4), 42-49. https://doi.org/10.26634/jpr.2.4.5947

Abstract

References

Full Article (HTML)

Pdf

* Research Scholar, Department of Computer Science and Engineering, JNTU Hyderabad, Andhra Pradesh, India.

** Principal & Professor, Holy Mary Institute of Technology and Science, Hyderabad, Andhra Pradesh, India.

* PG Scholar, Department of Computer Science and Engineering, M. Kumarasamy College of Engineering (Autonomous), Karur, India.

** Dean, Department of Computer Science and Engineering, M. Kumarasamy College of Engineering (Autonomous), Karur, India.

Ujwal Singh Vohra* , Shriprakash Dwivedi, Hardwari Lal Mandoria*

Navneet Kr. Kashyap* , B.K.Pandey, Hardwari Lal Mandoria*, Ashok Kumar****

* PG Scholar, Department of Information Technology, G.B.P.U.A & T, Pantnagar, India.

, Assistant Professor, Department of Information Technology, G.B.P.U.A & T, Pantnagar, India.

* Professor and Head, Department of Information Technology, G.B.P.U.A & T, Pantnagar, India.