足球篮球竞彩 :CIMSՓĵ

򾺲 www.fasfz.com  

چxӈDĹ̈DDַȡ

ע⣺Փڡ̈DWW(2000.21(2):9095)s־l
ʹՈעՓij̎

W
(BWCе̌WԺCAD&CGо B 116024)

ժҪ ̈DDĈDķָһҪ}ژxӈDAρȡַDķxȏĆxкYxַPMгxȻַPcvӈDַPȡַMһYxַcГMַַÛཻжùַӾļDΌַMжˮƽַDˮƽxӈDԱ_׺cؓmReʂ㷨^õַ̎cDεճB}ȡЧܺ܉ַw

1 

̈DDRecĿǰWg͹̽оğcڙCеϢϵyȑIоҪČHxDЃɲϢһLjDɎ׺ΈDؽMÁ_aƷwÁxaƷߴ缰ϢеĸڈDߴ电ֵҲЪڵ缼gҪLjDзdzҪϢ̈DDַȡcReһҪ}MһijߴDȸߌӴⶼ^̈DеְNַhĸַ̖r^sԼһЩcַ֌һSͬӡˢwжNHˮƽеĴֱ߀NǶȵбֱwcбwЕrַcַDճBˈDķָַȡyλ÷ɢСһrַķָRer횿]̈DַȡReһʮyĆ}

bڹ̈Dַc̎cęnкܴeһ̎^ǣȘRBͨwxַٸַ͹ַД෽ȻַָMַReУ^ӧDַķָRe㷨жNһǻBͨw[1]ǻ݆ۙ[2]ͬ߅ԙzyM݆ۙxַַ݆ͨ^MַReI֪RУг̾aƥ䷨[3]ÈDָָcַDճBַ

һNچxӈDĈDķָڶֵDˮƽγ̾aAPγ̾ؓһžsɗlжxѫ@Æx򣺾͈AӈDַPԱʾһxַPL^Сһ@ЩcĆxкYxַPMгDķxַPӵַPcͨ^vӈDӵַPȡַַYcDԪe^ַԌFַMһYxַӾ΁RַĴСλַӾཻжַټַГַDַǸڈDεxԺ׫@ȡɵÈDηùַӾļDΌַMжȻ󌢷ˮƽַDˮƽMˮƽγ̾aԆxӈD_ַĽYmReʂMԔB

2   DĆxӈD

ڹ̈DЈDԪַDԪжN羀AA^҈DԪཻͬDԪҪһNYyһȻͨ^׺cؓ_́MʸcϢȡͬһDԪͬDԪ֮gؓPϵҪָxַɹPMɵȡPĻAMReһNܺõķPȡy^FЌDַRe̎픵ֺĸ^漰h҇DдhϢ߀ДּĸҪһNģܱ_׺Δ߀ؓPϵڸNȡ

FЈDķx㷨؞ԪMBͨwBͨwmReؕI^СnjDԪRe@ҪһNģ܉yһDκֵĎ׺cؓϢ?ԑVͼģ?ԽyһDԪȡDԪ߀ַReĹPȡṩloxӈDĽ

Dˮƽγ̾a@rDķԪ׃γγBͨԷӳˈDؓPϵD1.bʾͨ^γ̵ؓPϵԫ@ȡDxγ[4]@ЩPIγ̞DԪָṩPγ̻ڌȺؓһԿԾۺϞһ_ijһ׺cؓxQ֮ll^wԺͺ^[5]еėlжxɾ͈AMxѵÆx@rDı_Ԫ׃xD1.cʾxĩγ̵ؓt_ˆxؓPϵxӈDر_DЈDԪcַĎ׺cؓϢD2ʾ

wpeD.jpg (29597 ֹ)

3  ַȡ

Dķx㷨жNҪǻBַͨһBͨĸhֶBͨɎׂBͨ򘋳ɵַSc׺ΈDԪͬڌHжذMַȡoԶNַД_JַLjDСӾԘRַĴСcλ㷨ҪǷBͨ_ַBͨwrڈDճBrݱγһwҪmָɈD`@ҪγBͨwrֻǻ܉ڴ_PĻAMЈDķָַȡtoɆ߷ָȡЧ

ƈDҎtDеĝhĸwҎһԴ_ַķֵĶ_ַPLַPǻһµ@ɂlĆxxȡַPӵַPһַһַIJֹPַȡַPAvӈDӵַPow㷨

1ȡδLַPO鮔ǰ½ַӾǰַ

2ǰoӵĿLtD4tȡǰӵδLַPO鮔ǰַַӾL

3أ2

4һַ

ַYcDԪe^ַ[1]FַMһYxD3.aoD1.aַȡYӾΘR

wpeE.jpg (25909 ֹ)

4 ַ

̈DַԴʽMַ_Zxַַw܉ַReṩĵϢڈDַĶַķ_ҲַЫ@ȡСȺСc߀•ַֻв_ДַĽM^ַȡ֮ıȻҪ

ַɽMͨõķЃɷNһNHough׃QMйzy[6]һNzyַɾnjַMһڶNжNһNַӾεľГcijһֵ^СֵtwͬһַһMˮƽֱɂַMбַӾεľжֵͬòֵͬ[2]һNַӾεĽcГcijһֵ^Сֵtwͬһַ[7]ֵcPϵ^ЕrҪͬr̎Nr

ʹͬһwڷͬˮƽֱбȡֵͬ^IJһN󽻷MַД^õؽQַД}oAO

4.1ַM

ڈDͬС֮ggDzͬgcСһPϵĸַӾmgͨ^ÛַӾַӾΰgUÛÛM\жɂַǷͬһַַ߀ַoַMϲE

1ȡδLַi O鮔ǰ½ַǰַ

2ǰoҹĿLtD4tȡǰҹδLַO鮔ǰַ

3أ2

4һַ

D3.bԿ󽻷^ýQַMφ}ַLПoAO

4.2ַD

̈DַǶ䶨}ֱӰReַķĻַB_ַ^t`^ڹ̈DַڈDǾcεķһCеDеijߴ电ڲÆxӈDDЈDκĆxпԺ׫@ÈDεķϢڸڈDεַԲñ^ַľc΁_ַĻַijһx򣨾򣩵ķcַַBһtþַַĶ}ɲīI[1]

ˮƽֱַȡÿַMReַȌÿַ䷽DˮƽȻMReD3.boַRD3.coDY

5  ַxʾ

wpeF.jpg (17667 ֹ)

҇DĻַReȡ횿]hcP@ַReȡܶ܉ȡPtضReЧ[8]ÆxӈD^õȡ_ַĎ׺cؓϢD4oַxʾ

6  YZ

IJÆxӈD_DϢڴ_PĻAMЈDķָַȡwԺ܏о㷨ѱ҂_lĹ̈DDRecϵyַ֮ȡЧ^MһоNsrַPȡоNYhMԹPARe

īI

[1] usʿ׵. ַճB־ཻķָcRe. ܛW199910(3)241-247
[2] mS仱. ̈Dַx͘עַɼg. AWW199725330-33
[3] . ̈DճBַȡcָ. AWW199624423-26
[4] S. Di Zenzo, L. Cinque, and S. Levialdi. Run-Based Algorithms for Binary Image Analysis and Processing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996
18(1)83-89
[5] Q. ڗlYĒDReՓcоʿWλՓģ. BBW1999.6
[6] ex܊. ̈DDDԄӷָSegChar. ܛW1999106:589-594
[7] Ⱥ. һNµַȡͽM㷨. ̈DWW1997No.2-338-45
[8] L. Y. Tseng and C. T. Chuang. An efficient Knowledge-Based Stroke Extraction Method for Multi-Font Chinese Characters. Pattern Recognition, 1992, 25(12): 1445-1458

An Algorithm of Extracting Characters from Scanned Image of Engineering Drawings Using Primitive Region Adjacency Graph

Zhang Xiwen, Ou Zongying
(Institute of CAD&CG, School of Mechanical Engineering, Dalian University of Technology, Dalian 116024)

Abstract It is a important issue how to separate text from scanned image of engineering drawings. The paper presents an algorithm to extract characters and their features from images using Primitive Region Adjacency Graph. We can easily get character stroke regions from primitive regions. At start of a character stroke region, a character region grows by traversing the graph for adjacent character stroke regions. After analyzing features of character regions, we can get real character regions. A string can be got through combining those near and collinear character regions. If rectangles inflated of two character regions are intersected, they are near. The direction of a string region is attained by the center points of enclosed rectangles of characters shared by the string and figures attached. Then characters not horizontal are rotated to horizontal. A Primitive Region Adjacency Graph can represent geometrical and topological features of a character region, which is helpful to extract features of a character region. Some applications show that the algorithm can deal with adherence of characters to graphics, and is effective and robust.
Key words Primitive Region Adjacency Graph, text-graphics separation, character stroke regions, character extraction, feature extraction

ߺ飺

19719ʿоģʽRe͈D

WʿҪCADD˹κСо


cu

gӭӑՓlՓļоIĿ
(Ոڰlԕrژ}ʹcuՓĵ}Ŀо@ӷҞg[)

| CIMSՓ | й | ̓M | | Փ | Ŀ_l | WgYԴ | վȫ | MՓľWվȫ |

line.gif (4535 ֹ)

˸õĞҷgӭӱվͶƱ{

򾺲 Ոc

վ򾺲 gӭL

ע⣺վδSDd

All rights reserved, all contents copyright 2000-2019
վ20003¿WL