Hidden Markov Model Based Odia Numeral Recognition Using Moment and Structural Features
Om Prakash Jena1, Sateesh Kumar Pradhan2, Pradyut Kumar Biswal3, Alok Ranjan Tripathy4, Sradhanjali Nayak5

1Om Prakash Jena, Department of Computer Science, Ravenshaw University, Cuttack, India.
2Sateesh Kumar Pradhan, Department of Computer Science, Utkal University, Bhubaneswar, India.
3Pradyut Kumar Biswal, Department of ECE, International Institute of Information Technology (IIIT) Bhubaneswar, India.
4Alok Ranjan Tripathy*, Department of Computer Science, Ravenshaw University, Cuttack, India.
5Sradhanjali Nayak, Department of Computer Science, Utkal University, Bhubaneswar, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 2317-2325 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8614088619/2019©BEIESP | DOI: 10.35940/ijeat.F8614.088619
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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: Optical character recognition (OCR) is a strategy to perceive character from optically checked and digitized pages. OCR plays an important role for Indian script research. The official language of the state Odisha is Odia. OCR face an incredible difficulties to recognize Odia language due to similar shape characters, their complex nature, the complicated way in which they combine form to compound character, use of Matra etc. Each character and numbers are passed through several modules like binarization, noise removal, segmentation, line segmentation, word segmentation, skeletonization, deskewing, thinning, thickening. The input picture is standardized to a size of 50 x 50 2D pictures. HMM is a stochastic process which has utilized in various applications for example speech recognition, Handwriting recognition, Gesture recognition. In this paper we utilized HMM to recognize the Odia character and numbers. Hidden Markov Model have many advantages such as resistant to noise, handle contrast recorded as a hard copy and the HMM devices are effectively accessible. In our proposed method we have developed an efficient recognition algorithm using Hidden Markov model based on moment based and structural feature to recognize Odia characters and numerals.
Keywords: Odia Numerals, Hidden Markov model, Moment based Feature, Structural Feature.