Classification in Devanagari Script using Elliptical Region-wise Features

2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) Pub Date : 2018-12-01 DOI:10.1109/ICSCCC.2018.8703200

Rajib Ghosh, Shaktideo Kumar, Prabhat Kumar

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引用次数: 1

Abstract

In this article, an attempt has been made to develop a system for classification of online handwritten text and non-text data from within a single online handwritten document in the most popular Indic script-Devanagari. As per our knowledge, no recognized work exists for handwritten text and non-text document classification in online mode in any Indic script. To develop this system an elliptical region-wise feature extraction approach has been proposed in this article. In this approach, each online stroke information of text and non-text documents is divided into smaller elliptical regions by constructing several concentric ellipses around the stroke. Each elliptical region is further divided into several sub-regions before extracting various structural and directional features of stroke portions from each sub region. These features are then studied in Hidden Markov Model (HMM) based classification platform. The efficiency of the present system has been measured on a self-generated dataset and it has provided promising result.

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基于椭圆区域特征的梵文文字分类

在本文中，尝试开发一个系统，用于对最流行的印度语——devanagari的单个在线手写文档中的在线手写文本和非文本数据进行分类。据我们所知，在任何印度文字的在线模式下，手写文本和非文本文档分类都没有公认的工作存在。为了开发该系统，本文提出了一种椭圆区域特征提取方法。该方法通过在笔画周围构造若干同心椭圆，将文本和非文本文档的每个在线笔画信息划分为更小的椭圆区域。将每个椭圆区域进一步划分为几个子区域，然后从每个子区域提取笔划部分的各种结构和方向特征。然后在基于隐马尔可夫模型(HMM)的分类平台上对这些特征进行研究。在一个自生成的数据集上测试了该系统的效率，并提供了令人满意的结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC)

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