IEEE Trans Pattern Anal Mach Intell - Robust Text Detection in Natural Scene Images.

Tópicos

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{ error(1145) method(1030) estim(1020) }
{ motion(1329) object(1292) video(1091) }
{ featur(3375) classif(2383) classifi(1994) }
{ extract(1171) text(1153) clinic(932) }
{ method(1969) cluster(1462) data(1082) }
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{ activ(1452) weight(1219) physic(1104) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ control(1307) perform(991) simul(935) }
{ howev(809) still(633) remain(590) }
{ import(1318) role(1303) understand(862) }
{ measur(2081) correl(1212) valu(896) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
{ treatment(1704) effect(941) patient(846) }
{ algorithm(1844) comput(1787) effici(935) }
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{ search(2224) databas(1162) retriev(909) }
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{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
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{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
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{ process(1125) use(805) approach(778) }
{ method(2212) result(1239) propos(1039) }

Resumo

Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks. In this paper, we propose an accurate and robust method for detecting texts in natural scene images. A fast and effective pruning algorithm is designed to extract Maximally Stable Extremal Regions (MSERs) as character candidates using the strategy of minimizing regularized variations. Character candidates are grouped into text candidates by the single-link clustering algorithm, where distance weights and clustering threshold are learned automatically by a novel self-training distance metric learning algorithm. The posterior probabilities of text candidates corresponding to non-text are estimated with a character classifier; text candidates with high non-text probabilities are eliminated and texts are identified with a text classifier. The proposed system is evaluated on the ICDAR 2011 Robust Reading Competition database; the f measure is over 76%, much better than the state-of-the-art performance of 71%. Experiments on multilingual, street view, multi-orientation and even born-digital databases also demonstrate the effectiveness of the proposed method. Finally, an online demo of our proposed scene text detection system has been set up at http://kems.ustb.edu.cn/learning/yin/dtext.

Resumo Limpo

text detect natur scene imag import prerequisit mani contentbas imag analysi task paper propos accur robust method detect text natur scene imag fast effect prune algorithm design extract maxim stabl extrem region mser charact candid use strategi minim regular variat charact candid group text candid singlelink cluster algorithm distanc weight cluster threshold learn automat novel selftrain distanc metric learn algorithm posterior probabl text candid correspond nontext estim charact classifi text candid high nontext probabl elimin text identifi text classifi propos system evalu icdar robust read competit databas f measur much better stateoftheart perform experi multilingu street view multiorient even borndigit databas also demonstr effect propos method final onlin demo propos scene text detect system set httpkemsustbeducnlearningyindtext

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