与OCR算法分析的比较研究和字符识别方法的发明分析

Santosh Kumar Henge, B. Rama
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引用次数: 7

摘要

OCR是文本字符处理识别和基于模式的图像识别中最活跃、最有趣的评价发明。在目前的生活中,OCR已经成功地应用于金融、法律、银行、医疗保健和家用电器。OCR由图像前采集、分类、后采集、前级处理、分段处理、后级处理、特征提取等不同层次的处理方法组成。许多研究人员在现代和传统技术的帮助下,在不同的语言版本中提出了不同层次的不同方法和方法。本文对各种字符识别方法和方法进行了详细的研究和分析:详细介绍了所使用的方法的流程和类型,在技术支持下建立的算法类型,实现了所提出方法的背景和每种方法的发明最佳结果流程。本文还阐述了各种OCR算法的主要目标和思想,如神经网络算法、结构算法、支持向量算法、统计算法、模板匹配算法,以及它们是如何对字符和符号进行分类、识别、规则形成、推理等识别的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comprative study with analysis of OCR algorithms and invention analysis of character recognition approched methodologies
OCR is the most active, interesting evaluation invention of text cum character processing recognition and pattern based image recognition. In present life OCR has been successfully using in finance, legal, banking, health care and home need appliances. The OCR consists the different levels of processing methods like as Image Pre Acquisition, Classification, Post-Acquisition, Pre-Level processing, Segmented Processing, Post-Level processing, Feature Extraction. The many researchers are proposed various levels of different methodologies and approaches in different versions of languages with help of modern and traditional technologies. This paper expressed the detail study and analysis of various character recognition methods and approaches: in details like as flow and type of approached methodology was used, type of algorithm has built with support of technology has implemented background of the proposed methodology and invention best outcomes flow for the each methodology. This paper and also expressed the main objectives and ideology of various OCR algorithms, like as neural networks algorithm, structural algorithm, support vector algorithm, statistical algorithm, template matching algorithm along with how they classified, identified, rule formed, inferred for recognition of characters and symbols.
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