Recognition of Isolated Handwritten Latin Characters using One Continuous Route of Freeman Chain Code Representation and Feedforward Neural Network Classifier

In a handwriting recognition problem, characters can be represented using chain codes. The main problem in representing characters using chain code is optimizing the length of the chain code. This paper proposes to use randomized algorithm to minimize the length of Freeman Chain Codes (FCC) generated from isolated handwritten characters. Feedforward neural network is used in the classification stage to recognize the image characters. Our test results show that by applying the proposed model, we reached a relatively high accuracy for the problem of isolated handwritten when tested on NIST database.

Authors:

• Dewi Nasien
• Siti S. Yuhaniz
• Habibollah Haron

Keywords:

• Handwriting Recognition
• Freeman Chain Code andFeedforward Backpropagation Neural Networks.

References:

[1] Tay, Y.H., Offline Handwriting Recognition using Artificial Neural
Network and Hidden Markov Model. PhD Thesis. Universiti Teknologi
Malaysia, 2002.
[2] Al-Rashaideh, H., Preprocessing Phase for Arabic Word Handwritten
Recognition. Information Transmission in Computer Network. 2006.
[3] Steinherz, T., Rivlin, E., Intrator, N., Off-Line Cursive Script Word
Recognition - A Survey. International Journal on Document Analysis and
Recognition. 1999.
[4] Mingqiang, Y., Kidiyo, K., Joseph, R., A Survey of Shape Feature
Extraction Techniques. Book Pattern Recognition (Edited by: Peng-
Yeng Yin). November 2008. IN-TECH.
[5] Wegener, I., Towards a Theory of Randomized Search Heuristics.
Lecturer Notes in Computer Science. Springer. 2003.
[6] Ravichandran, A., An Algorithm for Thinning Noisy Images.
International Conference on Acoustics Speech and Signal Processing,
1990.
[7] Alba, E., Chicana, J.F., Training Neural network with GA Hybrid
Algorithm. Proc of GECCO-04. Seattle, Washington. 2004.
[8] Chen, T-P, Tian.J-X., The Research of Artificial Neural Network as a
Tool of Adjustment Prediction in Eco-City Construction. Proceedings of
the Fourth International Conference on Machine Learning and
Cybernetics, Guangzhou, 18-21 August 2005.