[计算机科学] 手写体多特征组合提取技术 Devnagari字符识别 [推广有奖]

摘要翻译：
本文提出了一种手写Devnagari字符的OCR算法。基本符号由神经分类器识别。我们使用了四种特征提取技术，即相交、阴影特征、链码直方图和直线拟合特征。对字符图像进行全局阴影特征计算，对字符图像进行分段，计算相交特征、链码直方图特征和直线拟合特征。采用加权多数投票技术将四个基于多层感知器(MLP)的分类器获得的分类决策进行组合。在4900个样本的实验中，当我们考虑前五个选择的结果时，总的识别率为92.80%。将该方法与其它手写体Devnagari字符识别方法进行了比较，结果表明该方法具有较好的识别成功率。
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英文标题：
《Combining Multiple Feature Extraction Techniques for Handwritten
  Devnagari Character Recognition》
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作者：
Sandhya Arora, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar
  Basu, and Mahantapas Kundu
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最新提交年份：
2010
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  In this paper we present an OCR for Handwritten Devnagari Characters. Basic symbols are recognized by neural classifier. We have used four feature extraction techniques namely, intersection, shadow feature, chain code histogram and straight line fitting features. Shadow features are computed globally for character image while intersection features, chain code histogram features and line fitting features are computed by dividing the character image into different segments. Weighted majority voting technique is used for combining the classification decision obtained from four Multi Layer Perceptron(MLP) based classifier. On experimentation with a dataset of 4900 samples the overall recognition rate observed is 92.80% as we considered top five choices results. This method is compared with other recent methods for Handwritten Devnagari Character Recognition and it has been observed that this approach has better success rate than other methods.
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PDF链接：
https://arxiv.org/pdf/1005.4032