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KANNADA ALPHABETS RECOGNITION WITH APPLICATION TO BRAILLE TRANSLATION

机译:KANNADA字母识别技术在Braillee翻译中的应用

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摘要

In this paper, an effort is made to apply optical character recognition (OCR) for Braille translation on Kannada characters. In general, OCR systems for Indian language are more complex due to larger number of vowels, consonants, and conjuncts and Indian languages are inflectional and agglutinative in nature. Specifically, characters of Kannada script have higher similarity in shape and higher variability across fonts, making recognition of characters a difficult task. A decision tree is developed in this research work. The main motivations are that decision trees provide a natural way to incorporate prior knowledge of domain and many Kannada characters have similar looking shapes. The similar looking characters can be grouped and then further partitioned into categories at various levels to effectively create a decision tree. To facilitate this, three modular classifiers are developed based on the nature of Kannada characters. These modular classifiers are employed at nodes of the decision tree. The Braille equivalent of Kannada characters is obtained by translation rules. An overall accuracy of classification and Braille translation of 93.80% is obtained.
机译:本文致力于将光学字符识别(OCR)应用于卡纳达语字符的盲文翻译。通常,由于大量的元音,辅音和合词,用于印度语的OCR系统更加复杂,并且印度语本质上是曲折的和粘连的。具体而言,卡纳达语脚本的字符在形状上具有更高的相似度,并且在字体之间具有更高的可变性,这使得字符识别成为一项艰巨的任务。在这项研究中开发了决策树。主要动机是决策树提供了一种自然的方式来合并领域的先验知识,并且许多卡纳达语字符具有相似的外观形状。可以将外观相似的字符进行分组,然后在各个级别将其进一步划分为类别,以有效地创建决策树。为了促进这一点,根据卡纳达语字符的性质开发了三个模块化分类器。这些模块化分类器用于决策树的节点。卡纳达语字符的盲文等效项是通过翻译规则获得的。获得的分类和盲文翻译的整体准确性为93.80%。

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