Uncertain Classification Decision for Handwritten Numerals Recognition

碩士 === 淡江大學 === 資訊管理學系 === 87 === A uncertain classification decision system for handwritten numerals recognition is proposed. A thinning algorithm is first used to obtain the skeleton of a numeral. A set of feature points are then detected. The skeleton can be decomposed into geometric primitives....

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Bibliographic Details
Main Authors:	Tai-Yuan Hsiao, 蕭泰源
Other Authors:	Hung-chang Lee
Format:	Others
Language:	zh-TW
Published:	1999
Online Access:	http://ndltd.ncl.edu.tw/handle/30689272295921333832

id	ndltd-TW-087TKU00396025
record_format	oai_dc
spelling	ndltd-TW-087TKU003960252016-02-01T04:13:05Z http://ndltd.ncl.edu.tw/handle/30689272295921333832 Uncertain Classification Decision for Handwritten Numerals Recognition 乏確式推斷決策與手寫數字辨識 Tai-Yuan Hsiao 蕭泰源碩士淡江大學資訊管理學系 87 A uncertain classification decision system for handwritten numerals recognition is proposed. A thinning algorithm is first used to obtain the skeleton of a numeral. A set of feature points are then detected. The skeleton can be decomposed into geometric primitives. Finally, we use the relationships between the primitives to recognize the numeral. For the digits which have the similar primitives, a membership function is used to decide the probability of each digit. The handwritten numerals extracted from the NIST Special Database 19 are used to test the system. The correct rate is 88.72%. Hung-chang Lee 李鴻璋 1999 學位論文 ; thesis 35 zh-TW
collection	NDLTD
language	zh-TW
format	Others
sources	NDLTD
description	碩士 === 淡江大學 === 資訊管理學系 === 87 === A uncertain classification decision system for handwritten numerals recognition is proposed. A thinning algorithm is first used to obtain the skeleton of a numeral. A set of feature points are then detected. The skeleton can be decomposed into geometric primitives. Finally, we use the relationships between the primitives to recognize the numeral. For the digits which have the similar primitives, a membership function is used to decide the probability of each digit. The handwritten numerals extracted from the NIST Special Database 19 are used to test the system. The correct rate is 88.72%.
author2	Hung-chang Lee
author_facet	Hung-chang Lee Tai-Yuan Hsiao 蕭泰源
author	Tai-Yuan Hsiao 蕭泰源
spellingShingle	Tai-Yuan Hsiao 蕭泰源 Uncertain Classification Decision for Handwritten Numerals Recognition
author_sort	Tai-Yuan Hsiao
title	Uncertain Classification Decision for Handwritten Numerals Recognition
title_short	Uncertain Classification Decision for Handwritten Numerals Recognition
title_full	Uncertain Classification Decision for Handwritten Numerals Recognition
title_fullStr	Uncertain Classification Decision for Handwritten Numerals Recognition
title_full_unstemmed	Uncertain Classification Decision for Handwritten Numerals Recognition
title_sort	uncertain classification decision for handwritten numerals recognition
publishDate	1999
url	http://ndltd.ncl.edu.tw/handle/30689272295921333832
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Uncertain Classification Decision for Handwritten Numerals Recognition

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