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Offline Urdu Numeral Recognition Using Non-Negative Matrix Factorization

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Author Affiliations

  • 1 Department of Computer Science, Federal Urdu University of Arts, Sciences and Technology, Karachi, PAKISTAN

Res. J. Recent Sci., Volume 3, Issue (11), Pages 98-102, November,2 (2014)

Abstract

By the rapid change and advancement in technology a need for processing and preserving many texts had been felt. These texts are either in hard copies or in handwritten form. Hand-written numerals, written in various languages and scripts, are an integral part of these texts. Several efforts have been made to recognize numerals and a variety of Optical Character Recognition (OCR) systems have been successfully implemented and marketed. Urdu numerals, as opposed to English numerals, are different due to their style and format of writing. Various methods have been proposed but majority of them only address computer typed numerals in different forms and sizes. Therefore we need to develop new and enhance existing handwritten Urdu numerals recognition systems due to their wide scale use and application in many fields. This research addresses the problem of handwritten offline numerals. A novel approach of Non-negative Matrix Factorization (NMF) for Urdu handwritten character recognition has been proposed in this research.

References

  1. Sagheer M.W., He C.L., Nobile N. and Suen C.Y.,Holistic urdu handwritten word recognition using support vector machine, Int. Conf. on Pattern Recognition (ICPR), 1900 –1903 (2010)
  2. Das N, Mollah A.F., Saha S. and Haque S.S., Handwritten arabic numeral recognition using a multi layer perceptron, National Conf. on Recent Trends in Inf. Sys., 200-203 (2006)
  3. Razzak M.I., Hussain S.A., Sher M. and Khan Z.S., Combining offline and online preprocessing for online urdu character recognition, Int. MultiConf. of Engineers and Computer Scientists, , 18–20 (2009)
  4. Harifi A. and Aghagolzadeh A., A new pattern for handwritten persian/arabic digit recognition, Int. Conf. on Info. Tech. (ICIT2004) , Istanbul, Turkey, (2004)
  5. Rajashekararadhya S.V. and Ranjan P.V., Efficient zone based feature extraction algorithm for handwritten numeral recognition of four popular south indian scripts, J. of Theoretical and Applied Info. Tech., 4(12), 1171–1181 (2008)
  6. Asthana S., Haneef F. and Bhujade R.K. , Handwritten multiscript numeral recognition using artificial neural networks, Int. J. of Soft Comput. & Engin., 1(1), 1–5 (2011)
  7. Sardar S. and Wahab A., Optical character recognition system for Urdu, Int. Conf. Info. and Emerg. Tech. (ICIET), 14-16 (2010)
  8. Pal U. and Sarkar A., Recognition of Printed Urdu Script, th Int. Conf. on Doc. Anal. and Recog. (ICDAR), , (2003)
  9. Akram M. and Hussain S., Word segmentation for urdu ocr system, th Workshop on Asian Language Resources, Beijing, China, 88-94 (2010)
  10. Alaei A., Pal U. and Nagabhushan P., A comparative study of persian/arabic handwritten character recognition, Int. Conf. on Frontiers in Hand-writing Recognition, (2012)
  11. Mozaffari S., Faez K. and Kanan H.R., Recognition of isolated handwritten farsi/arabic alphanumeric using fractal codes, Image Analysis and Interpre-tation, 6th IEEE Southwest Symposium, 104-108 (2004)
  12. Mowlaei A., Faez K. and Haghighat A.T., Feature extraction with wavelet trans-form for recognition of isolated handwritten farsi/arabic characters and numerals, 14th Int. Conf.Digital Signal Processing, , 923-926 (2002)
  13. Mozaffari S., Faez K. and Kanan H.R., Feature comparison between fractal codes and wavelet transform in handwritten alphanumeric recognition using svm classifier, 17th Int. Conf.Pattern Recognition, , 331-334 (2004)
  14. Husain S.A., Sajjad A. and Anwar F., Online urdu character recognition system, Conf. on Machine Vision Applications (IAPR MVA), Tokyo, Japan, 16-18 (2007)
  15. Sharif M., Shah J.H. Mohsin S. and Raza M., Sub-holistic hidden markov model for face recognition, Res. J. of Rec. Sci., 2(5), 10–14 (2013)
  16. KhanY.D., Ahmad F. and Khan S.A., A Survey on use of Neuro-Cognitive and Probablistic Paradigms in Patterm Recognition, Res. J. of Recent Sci., 2(4), 74-79 (2013)