[1]马少平,金奕江.基于多Agent系统的脱机手写体汉字识别[J].智能系统学报,2009,4(05):398-405.[doi:10.3969/j.issn.1673-4785.2009.05.003]
 MA Shao-ping .,JIN Yi-jiang ...Offline recognition of hand-written Chinese characters based on a multi-Agent system[J].CAAI Transactions on Intelligent Systems,2009,4(05):398-405.[doi:10.3969/j.issn.1673-4785.2009.05.003]
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基于多Agent系统的脱机手写体汉字识别(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第4卷
期数:
2009年05期
页码:
398-405
栏目:
出版日期:
2009-10-25

文章信息/Info

Title:
Offline recognition of hand-written Chinese characters based on a multi-Agent system
文章编号:
1673-4785(2009)05-0398-08
作者:
马少平123金奕江123
1.清华大学计算机科学与技术系,北京100084;2.清华大学智能技术与系统国家重点实验室,北京100084;3.清华大学清华信息科学与技术国家实验室(筹),北京100084
Author(s):
MA Shao-ping 12.3 JIN Yi-jiang 1.2.3
1. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China; 2.State Key Lab of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China; 3.Tsinghua National Laboratory of Information Science and Technology, Tsinghua University, Beijing 100084, China
关键词:
汉字识别多Agent系统类市场模型模糊综合辩论协商规则
Keywords:
hand-written chinese character multi-Agent system market-like model fuzzy synthetic debate-negotiation rules
分类号:
TP391.4
DOI:
10.3969/j.issn.1673-4785.2009.05.003
文献标志码:
A
摘要:
由于脱机手写体汉字的多样性和随意性,识别起来具有很大的难度,依靠单一的特征很难实现高准确率的识别.引入多Agent的概念,将多种知识统一于多Agent系统之中,给出了一个面向脱机手写体汉字识别的多Agent类市场模型,提出了一种模糊综合方法和辩论协商规则,实现了一个基于多Agent系统的脱机手写体汉字识别系统.初步测试结果显示出系统的有效性.
Abstract:
Due to the diversity and randomness of Chinese characters, it is difficult for offline handwritten Chinese character recognition to perform well when based solely on analysis of a single feature. In order to solve this problem, a multiAgent based recognition method was proposed. It merges a variety of knowledge into a marketlike model. A comprehensive approach using fuzzy rules to provide consultation and debate rules between Agents was also incorporated. With this proposed method, a multiAgent offline handwritten Chinese character recognition system was constructed. Preliminary experimental results showed the effectiveness of this system.

参考文献/References:

[1]陈   静,穆志纯,孙筱倩. 计算机模拟汉字字形认知过程的研究[J]. 智能系统学报, 2008, 3(3): 216-221.
CHEN Jing, MU Zhichun, SUN Xiaoqian. Computer simulation of the cognition of Chinese characters[J]. CAAI Transactions on Intelligent Systems, 2008, 3(3): 216-221.
[2]张忻中. 汉字识别技术[M]. 北京:清华大学出版社,1992: 31-41, 125-160.
[3]DU Qingdong, LIU Jie. A new neural fusion recognition method with multi-Agent[C]//Proceedings of the Third International Conference on International Information Hiding and Multimedia Signal Processing (IIH-MSP 2007). Washington DC: IEEE Computer Society, 2007: 127-130.
[4]ZHU Xiaoyan.Multiple neural networks model and its application in pattern recognition[C]//IEEE International Conference on Neural Information Processing.Beijing, China, 1995: 966-969.
[5]张永慧,刘昌平,罗    公,等.技术综合集成在模式识别中的应用[J].计算机学报, 1995, 18(19): 678-685.
ZHANG Yonghui, LIU Changping, LUO Gong, et al. Integration comprehensive techniques in pattern recognition[J]. Chinese Journal of Computers, 1995, 18(19): 678-685.
[6]马少平,夏   莹,朱小燕. 基于模糊方向线素特征的手写体汉字识别[J]. 清华大学学报:自然科学版,1997, 37(3): 42-45.
MA Shaoping, XIA Ying, ZHU Xiaoyan. Handwritten Chinese characters recognizing based on fuzzy directional line element feature[J]. Journal of Tsinghua University: Sci & Tech, 1997, 37(3): 42-45.
[7]夏    莹,马少平,常新功,等. 基于统计的汉字识别文本的自动后处理方法[J]. 模式识别与人工智能, 1996, 9(2): 172-178.
XIA Ying, MA Shaoping, CHANG Xingong, et al. The method of automatic postprocessing based statistical probabilities for Chinese recognition text[J]. Pattern Recognition and Artificial Intelligence, 1996, 9(2): 172-178.
[8]RUSSELL S, NORVING P. 人工智能——一种现代方法[M]. 姜   哲,金奕江,张    敏,等,译. 2版. 北京:人民邮电出版社,2004: 26-42.
[9]LIU Jiming. 多智能体原理与技术[M]. 靳小龙, 张世武, LIU Jiming,译. 北京:清华大学出版社, 2003: 1-17, 43-64.
[10]HUYNH T D, JENNINGS N R, SHOADBOLT N R. An integrated trust and reputation model for open multi-Agent systems[J]. Autonomous Agents and Multi-Agent Systems, 2006, 13(2): 119-154.
[11]PANAIT L, LUKE S. Cooperative multi-Agent learning: the state of the art[J]. Autonomous Agents and MultiAgent Systems, 2005, 11(3): 387-434.
[12]王立春,陈世福. 多Agent多问题协商模型[J]. 软件学报, 2002, 13(8): 1637-1643.
 WANG Lichun, CHEN Shifu. A multiAgent multi-issue negotiation model[J]. Journal of Software, 2002, 13(8): 1637-1643.
[13]张德喜,马少平,朱绍文,等. 基于统计与神经元方法相结合的手写体相似字识别[J]. 中文信息学报, 1999, 13(3): 33-39.
ZHANG Dexi, MA Shaoping, ZHU Shaowen, et al. Handwritten similar Chinese characters recognition based on combining statistics with neural networks method[J]. Journal of Chinese Information Processing, 1999, 13(3): 33-39.

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备注/Memo

备注/Memo:
作者简介:
马少平, 男, 1961年生, 教授, 博士生导师,主要研究方向为智能信息处理、信息检索、汉字识别与后处理以及中文古籍数字化.承担过多项国家自然科学基金、“863”项目、“973”项目及国际合作项目,在脱机手写体汉字识别和后处理方面达到了国际先进水平,“脱机手写体汉字与数字识别系统”1998年1月获得国家教委科技进步二等奖.发表学术论文70余篇,出版专著2部.

金奕江,男,1970年生,工程师,主要研究方向为汉字识别、信息检索与处理.发表学术论文10余篇.
更新日期/Last Update: 2009-12-29