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CHARACTER RECOGNITION METHOD AND SYSTEM USING HIPPOCAMPAL NEURAL NETWORK LEARNING ALGORITHM

机译:海马神经网络学习算法的字符识别方法及系统

摘要

A method and a system for recognizing a character with a hippocampal neural network learning algorithm are provided to maximize a character recognition ratio by recognizing the character from an image with the hippocampal neural network learning algorithm capable of increasing a recognition database regardless of the number of character recognition patterns/features. A scanning unit(20) forms a document image by scanning a document. A recognition unit(30) outputs a recognition result by receiving the document image from the scanning unit, extracting a character image of target characters from the document image, preprocessing/converting the extracted character image into text, and extracting features of the characters from the converted text. The recognition unit comprises a short-term recognition database(36) storing patterns having a value under a threshold set by an output frequency of the pattern, a long-term database(38) storing the patterns having the value over the threshold, and a processor(32). The processor outputs the recognition result by operating a recognition program(34), obtaining the pattern matched with the features of the extracted character with the short-term and long-term database.
机译:提供一种利用海马神经网络学习算法识别字符的方法和系统,以通过海马神经网络学习算法从图像中识别字符来最大化字符识别率,该算法能够增加识别数据库,而与字符数无关识别模式/功能。扫描单元(20)通过扫描文件形成文件图像。识别单元(30)通过从扫描单元接收文档图像,从文档图像中提取目标字符的字符图像,将提取的字符图像预处理/转换为文本,以及从字符提取特征来输出识别结果。转换后的文本。识别单元包括短期识别数据库(36),长期识别数据库(36),该短期识别数据库(36)存储模式值的阈值以下的模式,该阈值由模式的输出频率设定;处理器(32)。处理器通过操作识别程序(34)输出识别结果,并通过短期和长期数据库获得与所提取字符的特征相匹配的模式。

著录项

  • 公开/公告号KR100894870B1

    专利类型

  • 公开/公告日2009-04-24

    原文格式PDF

  • 申请/专利权人

    申请/专利号KR20070016335

  • 发明设计人 하순호;김영호;

    申请日2007-02-16

  • 分类号G06K9/36;G06K9/62;G06T7/40;

  • 国家 KR

  • 入库时间 2022-08-21 19:12:02

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