孟加拉文和梵文手写体与罗马体混合文本的文字级识别
Word level Script Identification from Bangla and Devanagri Handwritten Texts mixed with Roman Script
摘要：
印度是一个多语言的国家，在文本文档中，罗马字母通常与不同的印度字母一起使用。为了开发一个手写体光学字符识别（OCR）系统，必须正确识别手写体文本中的文字。在本文中，我们提出了一个系统，它自动分离手写文字的脚本从一个文件中，写在孟加拉语或Devanagri与罗马字母混合。在这种脚本分离技术中，我们首先使用脚本独立的相邻成分分析技术从文档页面中提取文本行和单词。然后，我们设计了一个基于多层感知器（MLP）的分类器，用于脚本分离，使用8个不同的词级整体特征进行训练。两个相等大小的数据集，一个与孟加拉语和罗马脚本和其他与Devanagri和罗马脚本，准备系统评估。在各自独立的文本样本上，词级脚本识别准确率分别达到99.29%和98.43%。
英文摘要：
India is a multi-lingual country where Roman script is often used alongside different Indic scripts in a text document. To develop a script specific handwritten Optical Character Recognition (OCR) system, it is therefore necessary to identify the scripts of handwritten text correctly. In this paper, we present a system, which automatically separates the scripts of handwritten words from a document, written in Bangla or Devanagri mixed with Roman scripts. In this script separation technique, we first, extract the text lines and words from document pages using a script independent Neighboring Component Analysis technique. Then we have designed a Multi Layer Perceptron (MLP) based classifier for script separation, trained with 8 different wordlevel holistic features. Two equal sized datasets, one with Bangla and Roman scripts and the other with Devanagri and Roman scripts, are prepared for the system evaluation. On respective independent text samples, word-level script identification accuracies of 99.29% and 98.43% are achieved.
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