Character Recognition Using Hidden Markov Models
Saish Bhende1, Kutub Thakur2, Jason Teseng3, Md Liakat Ali4, Nan Wang5
1Saish Bhende, Research Scholar, Pace University, NY, USA.
2Kutub Thakur, Assistant Professor, New Jersey City University, Jersey City, NJ, USA.
3Jason Teseng, Adjunct Professor, New Jersey City University, Jersey City, NJ, USA.
4Md Liakat Ali, Assistant Professor, Rider University, NJ , USA.
5Nan Wang, Associate Professor, New Jersey City University, Jersey City, NJ, USA.
Manuscript received on 13 December 2018 | Revised Manuscript received on 25 December 2018 | Manuscript Published on 24 January 2019 | PP: 105-110 | Volume-7 Issue-4S2 December 2018 | Retrieval Number: Es2046017519/19©BEIESP
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© The Authors. 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: The need for the character recognition in today’s real time world has motivated us to do study on the process of HMM’s character recognition. This technique is the finest and accurate one to get a word or a character or a line to be recognized almost at 100 percent success rate. This paper shows the different steps to be followed during the iterations for the recognition. The paper basically reflects the introduction to the procedure to the prediction to the typical errors to the success rate of the method.
Keywords: Recognition Models Real Time Prediction.
Scope of the Article: Pattern Recognition