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A Simple Segmentation Approach for Unconstrained Cursive Handwritten Words in Conjunction with the Neural Network.
Amjad Rehman Khan, Zulkifli Mohammad
Pages - 29 - 35     |    Revised - 06-08-2008     |    Published - 16-09-2008
Volume - 2   Issue - 3    |    Publication Date - June 2008  Table of Contents
Image analysis, Segmentation, Neural Network, Preprocessing, Pattern matching
This paper presents a new, simple and fast approach for character segmentation of unconstrained handwritten words. The developed segmentation algorithm over-segments in some cases due to the inherent nature of the cursive words. However the over segmentation is minimum. To increase the efficiency of the algorithm an Artificial Neural Network is trained with significant amount of valid segmentation points for cursive words manually. Trained neural network extracts incorrect segmented points efficiently with high speed. For fair comparison benchmark database IAM is used. The experimental results are encouraging.
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2 Saba, T., Rehman, A., Alkharj, K. S. A., & Al-Zahrani, S. Character Segmentation in Overlapped Script using Benchmark Database.
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5 Rehman, A., & Saba, T. (2014). Neural networks for document image preprocessing: state of the art. Artificial Intelligence Review, 42(2), 253-273.
6 Saba, T., & Rehman, A. (2013). Effects of artificially intelligent tools on pattern recognition. International Journal of Machine Learning and Cybernetics, 4(2), 155-162.
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15 Rehman, A. (2012). Machine learning based air traffic control strategy. International Journal of Machine Learning and Cybernetics, 1-11.
16 Rehman, A., & Saba, T. (2012). Off-line cursive script recognition: current advances, comparisons and remaining problems. Artificial Intelligence Review, 37(4), 261-288.
17 Saba, T., Alzorani, S., & Rehman, A. (2012). Expert system for offline clinical guidelines and treatment. Life Science Journal, 9(4), 2639-2658.
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23 Saba, T., Sulong, G., & Rehman, A. (2010). A survey on methods and strategies on touched characters segmentation. International Journal of Research and Reviews in Computer Science, 1(2), 103-114.
24 Angadi, S. A., & Kodabagi, M. M. (2009). A texture based methodology for text region extraction from low resolution natural scene images. International Journal of Image Processing, 3(5), 229-245.
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Mr. Amjad Rehman Khan
Department of Computer Graphics and Multimedia - Malaysia
Dr. Zulkifli Mohammad
Department of Computer Graphics and Multimedia - Malaysia