Buradasınız

Anasayfa » Content

Handwritten Hindi Numerals Recognition

Journal Name:

  • International Journal of Innovation and Applied Studies

Publication Year:

  • 2013

Key Words:

Author Name
Abstract (2. Language): 
The proposed method is efficient where it is new, simple, fast, accurate so it is used in this research for recognizing Hindi numerals (0,1,2,3,4,5,6,7,8,9), that are usually used by Arabic population. The method is effective with handwritten numerals. This method is simply depends on determining number of terminal points and its positions for each digit in its different shapes, that represent the main feature for recognition. Only five features are added when there are similarity between digits (have the same number of terminals and position), the additional features was: less pixels number to recognize digit zero, intersection point position to recognize digit (2,3,6,7) that have three terminal points, image width to recognize digit one, curve number to recognize digit (2,4) that have two terminal points finally closed shape feature is added to recognize special cases of digit five and nine that have irregular shapes. Hence the proposed method is based on structural primitives such as curve, line, point type and etc. in a manner similar to that in which human beings describe characters geometrically. This work deals with noisy object by removed them from the original image to ensure that the noise pixels not merge with the original digit pixels. Encouraged recognition results are obtained for handwritten numerals samples written by different persons, different ages, different pens type, also different size, digits with rotation state are tested that gave an excellent recognition results. Some of problems with digit 9,5 are solved.
Bookmark/Search this post with
FULL TEXT (PDF): 
PDF icon ijias-13-071-04.pdf
  • 1
310-317
  • English

REFERENCES

References: 

1] Robert T. Olszewski, "Generalized Feature Extraction for Structural Pattern Recognition in Time Series Data", Ph.D.
Thesis, School of Computer Science, Carnegie Mellon University, 2001.
[2] Seong-Whan Lee, ”Off-Line Recognition of Totally Unconstrained Handwritten Numerals Using Multilayer Cluster Neural
Network”, IEEE Trancactions on pattern analysis and machine intelligence,vol. 18, no. 6, June , 1996.
[3] Hermineh Sanossian ,"Feature Extraction technique for Hindi Numerals", Mu’tah University, P.O. Box 7 Mu’tah-
Jordan,1998.
[4] Hussein Al-Zoubi, Mahmood Al-Khassaweneh, " Offline Machine- Print Hindi Digit Recognition Using Translational
Motion Estimation", Computer Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk
University, Irbid 21163, Jordan,2008.
[5] Yun Li Mei Xie, "An Effective Method for Number and Letter Character Recognition", School of Electronic Engineering,
University of Electronic Science and Technology of China Chengdu China,11th. IEEE International conference on
communication technology proceedings,2008.
[6] Huda Mahdi Abbas, " Printed Arabic Character Recognition Using Neural Network", M.SC. thesis, Al-Nahrain University,
2011.
[7] Li Yuchen, Tian Zhen, You Jia, " Character feature extraction method and its effectiveness evaluation", North China
University of Technology, Beijing, 100144, China,2012.
[8] William K. Pratt, "Digital Image Processing", the Wiley biennials- knowledge for generation, fourth edition pixel soft,
INC, California, 2007.
[9] Daggu Venkateshwar Rao, shutiPatil, Navveen Anne Babu and Vmuthukumar "implementation and evaluation of image
processing algorithm on reconfigurable architecture using C-base hardware descriptive language", BS publishers and
distributors (India) Volume I Number 1, pp. 9-34, 2006.
[10] Raghad Khrebit Rashid Al-Khalidy "Arabic Character Recognition Based on Moment Method", M. Sc. Thesis, Al-Nahrain
University, 2008.
[11] B. Snakur, M. Sezgin, Thresholding techniques and quantitative Performance". evaluation, Journal Electron. Imaging,
Vol. 13, No.1, pp.146-165, 2004.
[12] John C. Russ “The Image Processing handbook”, CRC, Fifth Edition, 2007.
[13] Gonzales, R, C. and woods R.E. "Digital Image Processing”, addition- Wesley, New York, 1992.
[14] Jun-Sik Kwon., Jun-Woong Gi' and Eung-Kwan Kang, “An enhanced thinning algorithm using parallel
processing”, International Conference, Volume: 3, Pages: 752-755, 7-10 Oct. 2001.

Thank you for copying data from http://www.arastirmax.com