Odia Characters and Numerals Recognition using Hopfield Neural Network Based on Zoning Feature
Om Prakash Jena1, Sateesh Kumar Pradhan2, Pradyut Kumar Biswal3, Alok Ranjan Tripathy4 

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 Engineering and Communication Engineering, International Institute of Information Technology (IIIT) Bhubaneswar, India.
4Alok Ranjan Tripathy, Department of Computer Science, Ravenshaw University, Cuttack, India. 

Manuscript received on 05 March 2019 | Revised Manuscript received on 11 March 2019 | Manuscript published on 30 July 2019 | PP: 4928-4937 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3763078219/19©BEIESP | DOI: 10.35940/ijrte.B3763.078219
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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: Odia character and digits recognition area are vital issues of these days in computer vision. In this paper a Hopfield neural network designe to solve the printed Odia character recognition has been discussed. Optical Character Recognition (OCR) is the principle of applying conversion of the pictures from handwritten, printed or typewritten to machine encoded text version. Artificial Neural Networks (ANNs) trained as a classifier and it had been trained, supported the rule of Hopfield Network by exploitation code designed within the MATLAB. Preprocessing of data (image acquisition, binarization, skeletonization, skew detection and correction, image cropping, resizing, implementation and digitalization) all these activities have been carried out using MATLAB. The OCR, designed a number of the thought accuses non-standard speech for different types of languages. Segmentation, feature extraction, classification tasks is the well-known techniques for reviewing of Odia characters and outlined with their weaknesses, relative strengths. It is expected that who are interested to figure within the field of recognition of Odia characters are described in this paper. Recognition of Odia printed characters, numerals, machine characters of research areas finds costly applications within the banks, industries, offices. In this proposed work we devolve an efficient and robust mechanism in which Odia characters are recognized by the Hopfield Neural Networks (HNN).
Keywords: Feature Extraction, Hopfield Neural Network, Segmentations, Zoning Features.

Scope of the Article: Pattern Recognition