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Pattern Matching and Analysis of Drawn or Handwritten Digits Using Correlation

Harish E. Khodke, Dr.R.R. Manza

Abstract


The recognition of drawn digits is a challenging task in the field of image processing and pattern recognition. A drawn or handwritten digits recognition system using correlation matching algorithm for digits recognition and k-neighbor classifying algorithm to select the true digit. Database includes handwritten digit examples which are collected from a hundred persons. These digit images converted to binary type before added to the database. The proposed system will aid applications for postal/parcel digits recognition and conversion of any hand written document into structural text form. In this proposed system first image acquisition or drawn digits then input image is segmented into isolated digits by assigning a number to each digit using a labeling process. The features of the digits that are crucial for classifying them at recognition stage are extracted. Digit images converted to binary and using correlation matching algorithm for digits recognition and k-neighbor classifying algorithm to select the true digit.

Keywords


Drawn or Handwritten Digits Recognition, Related Work, Image Processing, Proposed Recognition System, Feature Extraction and K-Neighbor Classifying Algorithm.

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References


S. Mori, C.Y. Suen and K. Kamamoto, “Historical review of OCR research and development,” Proc. of IEEE, vol. 80, pp. 1029-1058, July 1992.

V.K. Govindan and A.P. Shivaprasad, “Character Recognition – A review,” Pattern Recognition, vol. 23, no. 7, pp. 671- 683, 1990.

R. Plamondon and S. N. Srihari, “On-line and off- line handwritten character recognition: A comprehensive survey,”IEEE. Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 63-84, 2000

U. Bhattacharya, and B. B. Chaudhuri, “Handwritten numeral databases of Indian scripts and multistage recognition of mixed numerals,” IEEE Transaction on Pattern analysis and machine intelligence, vol.31, No.3, pp. 444-457, 2009.

U. Pal, T. Wakabayashi and F. Kimura, “Handwritten numeral recognition of six popular scripts,” Ninth International conference on Document Analysis and Recognition ICDAR 07, Vol.2, pp.749-753, 2007.

Pal, U. and B.B. Chaudhuri, “Indian script character recognition: A survey,” Pattern Recognition, vol. 37, no.9, pp. 1887-1899, 2004.

Mantas, J, “An overview of character recognition Methodologies,” Pattern Recognition, Vol.19, Issue 6, pp. 425-430, 1986.

Dinesh Kumar, Neeta Rana,” Speech Synthesis System for Online Handwritten Punjabi Word: An Implementation of SVM & Concatenative TTS”, International Journal of Computer Applications (0975 – 8887) Volume 26– No.2, July 2011.

R.G. Casey and E.Lecolinet, “A Survey of Methods and Strategies in Character Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No.7, July 1996, pp. 690-706.

S.V. Rajashekararadhya, and P.Vanajaranjan, “Efficient zone based feature extraction algorithm for handwritten numeral recognition of four popular south-Indian scripts,” Journal of Theoretical and Applied Information Technology, JATIT vol.4, no.12, pp.1171-1181, 2008.

http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm.

B. V. K. Vijaya Kumar, Abhijit Mahalanobis and Richard Juday,"Correlation Pattern Recognition".

Alceu de Britto, S., R. Sabourin, F. Bortolozzi and Y. Ching Suen, 2003, “The recognition of handwritten numeral strings using a two-stage HMM-based method. Int. J. Docu. Anal. Recog.”, 5: 102-117. DOI: 10.1007/s10032-002-0085-5.

Palacios, R., A. Gupta and P.S. Wang, 2004. Handwritten bank check recognition of courtesy amounts. Int. J. Image Graph., 4: 1-20. http://dspace.mit.edu/handle/1721.1/7386.

Saleh Ali K. Al-Omari, Putra Sumari, Sadik A. Al-Taweel and Anas J.A. Husain,"Digital Recognition using Neural Network",Journal of Computer Science 5 (6): 427-434, 2009 ISSN 1549-3636.


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