Commenced in January 2007
Paper Count: 31014
Recognition of Isolated Handwritten Latin Characters using One Continuous Route of Freeman Chain Code Representation and Feedforward Neural Network Classifier
Abstract: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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1071728Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1503
 Tay, Y.H., Offline Handwriting Recognition using Artificial Neural Network and Hidden Markov Model. PhD Thesis. Universiti Teknologi Malaysia, 2002.
 Al-Rashaideh, H., Preprocessing Phase for Arabic Word Handwritten Recognition. Information Transmission in Computer Network. 2006.
 Steinherz, T., Rivlin, E., Intrator, N., Off-Line Cursive Script Word Recognition - A Survey. International Journal on Document Analysis and Recognition. 1999.
 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.
 Wegener, I., Towards a Theory of Randomized Search Heuristics. Lecturer Notes in Computer Science. Springer. 2003.
 Ravichandran, A., An Algorithm for Thinning Noisy Images. International Conference on Acoustics Speech and Signal Processing, 1990.
 Alba, E., Chicana, J.F., Training Neural network with GA Hybrid Algorithm. Proc of GECCO-04. Seattle, Washington. 2004.
 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.