Histograms of Points, Orientations, and Dynamics of Orientations Features for Hindi Online Handwritten Character Recognition

TL;DR

This paper introduces a novel set of features based on histograms of points, orientations, and their dynamics for online handwritten Hindi character recognition, achieving high classification accuracy.

Contribution

The study proposes a new feature extraction method that is invariant to stroke direction and order, improving recognition performance over existing features.

Findings

Proposed features outperform other features in classification accuracy.

Support vector machines trained with these features achieve 92.9% accuracy.

Features demonstrate better discriminative capability for Hindi characters.

Abstract

A set of features independent of character stroke direction and order variations is proposed for online handwritten character recognition. A method is developed that maps features like co-ordinates of points, orientations of strokes at points, and dynamics of orientations of strokes at points spatially as a function of co-ordinate values of the points and computes histograms of these features from different regions in the spatial map. Different features like spatio-temporal, discrete Fourier transform, discrete cosine transform, discrete wavelet transform, spatial, and histograms of oriented gradients used in other studies for training classifiers for character recognition are considered. The classifier chosen for classification performance comparison, when trained with different features, is support vector machines (SVM). The character datasets used for training and testing the classifiers consist of online handwritten samples of 96 different Hindi characters. There are 12832 and 2821 samples in training and testing datasets, respectively. SVM classifiers trained with the proposed features has the highest classification accuracy of 92.9\% when compared to the performances of SVM classifiers trained with the other features and tested on the same testing dataset. Therefore, the proposed features have better character discriminative capability than the other features considered for comparison.