Hidden markov model based odia numeral recognition using moment and structural features

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.