Open AccessOdia Characters and Numerals Recognition using Hopfield Neural Network Based on Zoning Feature
Author(s) -
Om Prakash Jena,
Sateesh Kumar Pradhan,
Pradyut Kumar Biswal,
Aruna Tripathy
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b3763.078219
Subject(s) - computer science , optical character recognition , artificial neural network , artificial intelligence , preprocessor , pattern recognition (psychology) , numeral system , character (mathematics) , feature extraction , feature (linguistics) , deep learning , segmentation , character recognition , image (mathematics) , mathematics , linguistics , philosophy , geometry
Odia character and digits recognition area are vital issues of these days in computer vision. In this paper a Hope field neural network design 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).