数字图像处理中尼泊尔文机印文字字符成分分割与分类

V. Joshi, S. Panday
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引用次数: 1

摘要

提取具有相关成分的核心字符,提取符号的准确性和一致性决定了任何数字图像处理系统的准确性。此外,对提取的符号进行预分类,大大减少了分类层次上的处理负荷。本文提出了一种机器印刷德文-尼泊尔文文本的核心字及其成分分割分类方法。本文还提出了一种提取与核心字无关的修饰语成分的方法。在这里,Shirorekha或Dika或标题行被认为是分割和分类的主要组成部分。该模型利用词的水平投影轮廓去除了Shirorekha或Dika,并对词的图像进行标记,提取物体作为组件。我们提供了一个对象有一个标签。一些角色在移除Shirorekha时失去了一些属性。从而对特征进行重构,保证提取对象的准确性和一致性。我们使用了一组结构布局-高度宽度比,在单词中的外观位置,在提取的符号上存在Shirorekha来对提取的对象进行分类。我们将提取的符号分为五类:非dika字符、正则字符、连词、上修饰语和下修饰语。结果表明,该方法的准确率为98.26% ~ 100%。
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Character Component Segmentation and Categorization of Machine Printed Text in Devanagari (Nepali) Script in Digital Image Processing
Extraction of core character with related components, exactness and consistency of extracted symbol determines accuracy of any Digital Image Processing Systems. Furthermore, pre-categorization of extracted symbols reduces lots of processing load at classification level. This paper proposes a core character and its component segmentation and categorization method of Machine Printed Text in Devanagari-Nepali Script. This paper also presents a method that extract modifier components which are not connected to core character. Here, Shirorekha or Dika or header line is considered as major component of segmentation and categorization. The proposed model removes the Shirorekha or Dika using horizontal projection profile on word and label the image of word to extract the objects as components. We have supplied one object has one label. Some character loose some property at removal of Shirorekha. Thus, we have reconstructed character for exactness and consistency of extracted object. We have used a set of structural layout - height width ratio, appearance position in word, presence of Shirorekha over the extracted symbol to categorize the extracted objects. We categorize extracted symbol into five categories - Non-dika character, regular character, conjuncts, upper modifiers and lower modifiers. The result obtained shows we have an accuracy of 98.26% to 100% as compared to previous method.
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