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Simulation of Zhang Suen Algorithm using Feed- Forward Neural Networks

Ritika Luthra, Gulshan Goyal. Published in Networks.

Communications on Applied Electronics
Year of Publication: 2015
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Ritika Luthra, Gulshan Goyal

Ritika Luthra and Gulshan Goyal. Article: Simulation of Zhang Suen Algorithm using Feed- Forward Neural Networks. Communications on Applied Electronics 2(5):9-15, July 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Ritika Luthra and Gulshan Goyal},
	title = {Article: Simulation of Zhang Suen Algorithm using Feed- Forward Neural Networks},
	journal = {Communications on Applied Electronics},
	year = {2015},
	volume = {2},
	number = {5},
	pages = {9-15},
	month = {July},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


Image Skeletonization plays a very crucial role in image processing as it has been used in various applications such as pattern recognition, fingerprint analysis, Signature verification etc. Image skeletonization process generates unit pixel width skeletons. Present paper considers feed forward neural network approach for simulation of Zhang-Suen algorithm. Network parameters are chosen based on experimentation. Values of MSE, PSNR, and Execution time are calculated for Gurumukhi characters. Performance graphs have been plotted.


  1. Abu-Ain W., Bataineh B., Abu-Ain T. and OmarK., “Skeletonization Algorithm for Binary Images”, Fourth International Conference on Electrical Engineering and Informatics(ICEEI) Elsevier, Vol. 11, pp.704-709, 2013.
  2. Padole G.V. and Pokle S. B., “New Iterative Algorithms for Thinning Binary Images” IEEE Third International Conference on Emerging Trends in Engineering and Technology, Vol. 7, pp. 166-171, 2010.
  3. Lam L, Lee S.W. and Suen C.Y., “Thinning methodologies-A comprehensive survey”, IEEE transactions on pattern analysis and machine intelligence, Vol. 14, No. 9, pp. 869-885, 1992.
  4. Chatbri H. and Kameyama K., “Using Scale Space Filtering to Make Thinning Algorithms Robust Against Noise in Sketch Images”, International Conference on Pattern Recognition letters Elsevier, Vol. 42, pp. 1-10, 2014.
  5. Sharma V. et al. “ A comprehensive study of artificial neural networks” International journal of advanced research in computer science and computer engineering Volume 2, Issue 10 pp. 278-284 2012
  6. Matsumoto T., Chua L.O. and Yokohama T., “Image Thinning with a Cellular Neural Network”, IEEE Journal on Circuits and Systems, Vol. 37, Issue 5, pp. 638-640, 1990.
  7. Datta A. et al “Shape Extraction: A Comparative Study Between Neural Network-Based and Conventional Techniques” International Journal of Neural Computing & Applications Springer pp. 343-355 1998
  8. Ahmed P., “A Neural Network based Dedicated Thinning Method”, International Journal of Pattern Recognition Letters Elsevier ,Vol. 16, Issue 6, pp. 585-590, 1995.
  9. Altuwaijri M. and Bayoumi A., “A Thinning Algorithm for Arabic Characters Using ART2 Neural Network”, IEEE Transactions on Circuits and Systems-II Analog and Signal Processing, Vol. 45, No. 2, pp. 260-264, 1998.
  10. Shang L. and Yi Z., “A Class of Binary Images using Two PCNNs”, International Conference on Intelligent Computing, Elsevier, Vol. 70, Issue4-6, pp. 1096-1101, 2007.
  11. Xu D. et al “ A novel approach based on PCNNs template for fingerprint image thinning” IEEE pp.115-119 2009
  12. Li Z., Wang R. and Zhang Z., “Modified Binary Image Thinning using Template based PCNN”, International Conference on Information Technology and Software Engineering Springer, Vol. 212, pp.731-740, 2013.
  13. Zhang P.G. “ Neural Networks for Classification: A Survey” IEEE transactions on systems, man, and cybernetics, vol. 30, no. 4, pp. 451-462 November 2000
  14. Zhang T.Y. and Suen C.Y., “A Fast Parallel Algorithm for Thinning Digital Patterns”, Communications of the Association of Computer Machinery (ACM), Vol. 27, No. 3, pp. 236-239, 1984.


Feed-forward, MSE, Neural Networks, OCR, PSNR, Skeletonization, Zhang and Suen