Off-line odia handwritten character recognition: An axis constellation model based research

Abstract

Handwritten Character Recognition is most challenging area of research, in which for various aspects a little enhancement can be always achieved. It is due to the irregularity of writing and shapes of different class user’s orientation affects the recognition rate. In this paper we have taken the complexity of Odia handwritten character recognition and successfully resolve with Principal Component Analysis (PCA). Here we had adopted a model in which the importance of symmetric axis chords in recognition of unconstrained handwritten characters is established. This symmetric axis chords are drawn along both row-wise and column-wise among the points one end to other. In addition to we have calculated the statistical feature as Euclidian distance, Hamilton distance which drawn from the midpoint of the symmetric chord to nearest pixel of the character. Apart from it we have also reported the angular values from the centroid of the image to the character pixel. This empirical model also harnessed the PCA over the feature set and perform the dimension reduction to the feature set which later termed as the key feature set. A certain series of experiment was carried on for the proper implementation of proposed technique, henceforth we have taken the standard Handwritten Database from various research institutes. Lastly on simulation analysis Radial Basis Function Neural Network (RBFNN) has been reported as to achieve high recognition rate through Gaussian kernel and a comparison among them has also reported here with.