Recognition of Off-Line Handwritten Devanagari Characters using Combinational Feature Extraction

Abstract

Handwritten Devanagari off-line characters recognition is a challenging task due to peculiarities involved in the writing styles of different person. We are collected features from 64 dimensional feature extraction techniques and shadow features for the Devanagari character recognition in this proposed scheme. These calculated features are used for further classification. Features which are used here for recognition means histograms of direction chain code of the contour points of the characters [4]. The features are classified using neural network and selected the best result by weighted majority voting technique. General TermsThe general terms are Neural Network classifier is used with MLP classifier. Keywords64 dimensional features, Shadow features, neural network and weighted majority voting technique.

A Study on the Effects of Combining Different Features for the Recognition of Handwritten Bangla Characters

“…The shadow feature computes the length of the shadow in each direction of projection. Projections along the vertical and horizontal directions were considered in this paper [9].…”

“…Moreover, in the case of character images, the general practice is for the opposite directed movements to be coded with the same value, that is, 0 • and 180 • are both considered as code 0, 45 • and 225 • are both coded as 1, 90 • and 270 • are both coded 2, and 135 • and 315 • are both coded as 3 [9]. This modification reduces the chain code to four values.…”

“…4 from the whole image and 16 from the four sub-images) were obtained for each image. The feature vector was then normalized to keep the magnitude of each feature within 0 and 1 [9].…”