An Optimal Binarization Algorithm Based on Particle Swarm Optimization
P. Subashini1, N. Sridevi2
1Dr. P. Subashini, Department of Computer Science, Avinashilingam Institute of Home Science and Higher Education for Women, Coimbatorte, India.
2N. Sridevi, Department of Computer Science, Avinashilingam Institute of Home Science and Higher Education for Women, Coimbatorte, India.
Manuscript received on August 10, 2011. | Revised Manuscript received on August 18, 2011. | Manuscript published on September 05, 2011. | PP: 32-36 | Volume-1 Issue-4, September 2011. | Retrieval Number: D078071411/2011©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Document binarization is an active research area for many years. Binarization algorithms play an important role in the preprocessing phase of any character recognition system. This paper compares several alternative binarization algorithms for handwritten documents, by evaluating their performance. The algorithms evaluated are, global thresholding, Otsu thresholding, Kittler-Illingworth and local thresholding, Niblack algorithm along with the proposed PSO algorithm. From the tests and results, we can wrap up with the assumption that the proposed algorithm shows improved results.
Keywords: Evaluation, Global thresholding, Image Binarization, Local thresholding, PSO.