Multi-font Multi-size Kannada Numeral Recognition Based on Structural Features

TL;DR

This paper presents a fast, size-invariant, and font-agnostic method for recognizing Kannada numerals using structural features, achieving 100% accuracy across multiple fonts and sizes without size normalization.

Contribution

Introduces a novel, thinning-free, size-invariant recognition method for Kannada numerals utilizing structural features and a K-nearest neighbor classifier.

Findings

Achieved 100% classification accuracy on 1150 numeral images.

Method operates efficiently with an average processing time of 0.1476 seconds.

Effective across 20 font styles and sizes from 16 to 50.

Abstract

In this paper a fast and novel method is proposed for multi-font multi-size Kannada numeral recognition which is thinning free and without size normalization approach. The different structural feature are used for numeral recognition namely, directional density of pixels in four directions, water reservoirs, maximum profile distances, and fill hole density are used for the recognition of Kannada numerals. A Euclidian minimum distance criterion is used to find minimum distances and K-nearest neighbor classifier is used to classify the Kannada numerals by varying the size of numeral image from 16 to 50 font sizes for the 20 different font styles from NUDI and BARAHA popular word processing Kannada software. The total 1150 numeral images are tested and the overall accuracy of classification is found to be 100%. The average time taken by this method is 0.1476 seconds.