Segmentation of Unconstrained Handwritten Hindi Words Using Polygonal Approximation

Kapil K. Upreti, Soumen Bag
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引用次数: 5

Abstract

Segmentation of unconstrained handwritten words into characters in an optically scanned document image data is an essential task and presents challenges to researchers with a wide variety of handwritings, large varieties of pen-types, poor image quality, and a lack of ordering information of strokes. This paper contributes methods for accurate full segmentation of Hindi word images into constituent characters and modifiers. It follows the polygonal approximation approach for the segmentation, and makes use of structural properties along with directional measures to determine segmentation points in Hindi word images. The main methodological contribution of this paper is the use of polygonal approximation technique for word segmentation which is based on certain structural properties of Hindi language. Second focus of this work lies on the fact that segmentation is done without removal of shirorekha which eliminates the complexities present in earlier works. Experiments on real-world data show that our novel method is always competitive and results in more top performances than any of the other measures.
基于多边形逼近的无约束手写体印地语词分割
在光学扫描文档图像数据中,将无约束手写文字分割成字符是一项重要的任务,但由于手写种类繁多、笔型繁多、图像质量差、笔画顺序信息缺乏等问题,对研究人员提出了挑战。本文提出了印地语词象的准确全分方法,包括组成词和修饰语。它遵循多边形近似方法进行分割,并利用结构属性和方向度量来确定印地语单词图像中的分割点。本文的主要方法贡献是基于印地语的某些结构特性,使用多边形近似技术进行分词。这项工作的第二个重点在于分割是在没有去除shirorekha的情况下完成的,这消除了早期作品中存在的复杂性。对真实世界数据的实验表明,我们的新方法总是具有竞争力,并且比任何其他方法都能产生更多的最佳表现。
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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