Summary
The paper describes a newly presented hybrid method for high efficiency in script image feature extraction. The recognition rate was about 82% for very large number of scripts per class. However, it has reached even 100% in some cases with a smaller number of scripts per class. The system contains two projection-based methods for image characteristics extraction presented by very simple feature vectors and one image descriptor. A specially worked out thinning algorithm for the recognition system has simplified the feature extracting procedure as it provides a continuous one-pixel width skeleton of the script, which is essential for the simple-projection approach.
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Saeed, K., Tabedzki, M. (2005). Intelligent Feature Extract System for Cursive-Script Recognition. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32391-0_27
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DOI: https://doi.org/10.1007/3-540-32391-0_27
Publisher Name: Springer, Berlin, Heidelberg
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