Buradasınız

Anasayfa » Content

HANDWRITTEN CHARACTER RECOGNITION SYSTEM USING ARTIFICIAL NEURAL NETWORKS

Journal Name:

  • Istanbul University Journal of Electrical & Electronics Engineering

Publication Year:

  • 2007

Key Words:

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
In this paper, a Handwritten Character Recognition system is designed using Multilayer Feedforward Articial Neural Networks. Backpropagation Learning algorithm is prefered for training of neural network. Training set occures of various Latin characters collected from different people. The characters are presented directly to the network and correctly sized in pre-processing. Recognition percentage of the system is higher than acceptable level. Input datas, network parametres and training period effect the result.
Bookmark/Search this post with
FULL TEXT (PDF): 
PDF icon arastirmax-handwritten-character-recognition-system-using-artificial-neural-networks.pdf
  • 1
309-313
  • English

REFERENCES

References: 

[1] Araokar, S. “Visual character recognition using artificial neural networks”. MGM’s College of Engineering and Technology, 2005.
[2] Erdem, O. A. , Uzun, E. “Turkish times new roman, arial, and handwriting characters recognition by neural network”. J. Fac. Eng. Arch. Gazi Univ., Vol 20, No 1, 13-19, 2005
[3] Mani, N., Voumard, P. “An optical character recognition using Artificial Neural Network”. IEEE,1996.
[4] Smagt, P. “A comparative study of neural network algorithms applied to optical character recognition”. ACM/ IEEE,2000.
[5] Salameh, W. A., Otair M. A. “Online handwritten character recognition using an optical backpropagation neural network”. Issues in Informing Science and Information Technology, 787-795. 2005.
[6] http://www.rgu.ac.uk

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