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57[Liang at al., 1996] J. Liang, J. Ha, R. M. Haralick and I. T. Phillips, Documentlayout structure extraction using bounding boxes of different entities. In:Proceedings of Workshop on Applications of Computer Vision, WACV '96(1996), 278-283.[Liang, 1999] Jisheng Liang, Document Structure Analysis and Performance Evaluation,University of Washington, 1999.[Likforman-Sulem et al., 2006] Laurence Likforman-Sulem, Pascal Vaillant andAliette de Bodard de la Jacopière, Automatic name extraction from degradeddocument images, Pattern Analysis & Applications 9, 2-3 (2006), 211-227.[Lin et al., 1996] Jenn-Yih Lin, Chi-Wei Lee and Zen Chen, Identification ofbusiness forms using relationships between adjacent frames, Machine Visionand Applications 9, 2 (1996), 56-64.[Linxia and Lee, 2010] Linxia Liao and Jay Lee, Design of a reconfigurableprognostics platform for machine tools, Expert Systems with Applications 37,1 (2010), 240-252.[Maoa et al., 2003] Song Mao, Azriel Rosenfeld and Tapas Kanungo, DocumentStructure Analysis Algorithms: A Literature Survey, In: Proceedings of SPIEElectronic Imaging (2003), 197-207.[Maruyama et al., 1999] Kenichi Maruyama, Makoto Kobayashi, Yasuaki Nakanoand Hirobumi Yamada, Cursive Handwritten Word Recognition byIntegrating Multiple Classifiers, In: Document Analysis Systems: Theory andPractice, Lecture Notes in Computer Sciences 1655 (1999), 735.[May et al., 2004] Adolf May, Benjamin Coifman, Randall Cayford and GregMerritt, Automated Diagnostics of Loop Detectors and the Data CollectionSystem in the Berkeley Highway Laboratory, University of California,Berkeley (2004).[Milewski and Govindaraju, 2006], Robert Milewski and Venu Govindaraju,Extraction of Handwritten Text from Carbon Copy Medical Form Images,In: Document Analysis Systems VII, Lecture Notes in Computer Science 3827(2006), 106-116.
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Page 3:
iiLyhenteetADRT = Adaptive Document
Page 7 and 8:
2Tässä tutkimuksessa tarkastellaa
Page 9 and 10:
42. Yleiskuvaus paperilomakkeiden d
Page 12: 7• skannaaminen• tietomalliin t
Page 16 and 17: 11Kuva 2. Esimerkki vinosta ja suor
Page 18 and 19: 13sä artefakti vai ei. Tiiviysarvo
Page 20 and 21: 15mioitava jo järjestelmän suunni
Page 22 and 23: 17Prosessoitaessa samankaltaisia lo
Page 24 and 25: 19sanoista verkko, jossa sanaan lin
Page 28 and 29: 236. Tekstintunnistaminen lomakkeen
Page 31 and 32: 26Tietokoneen voi myös ottaa avuks
Page 33 and 34: 28generaation kohdalla, jolloin tar
Page 35 and 36: 30Käsinkirjoitettu teksti sisält
Page 37 and 38: 32#include "AspriseOCR.h"char* proc
Page 39 and 40: 34kirjastoa ja yhdessä Caere OCR:n
Page 41 and 42: 36voi käyttäjälle tulla ongelmia
Page 43 and 44: 386.7.3. Muut verkkopalvelutWebOCR:
Page 45 and 46: 40tiedostoformaatit. Tiedostoformaa
Page 47 and 48: 42tä voi enää käytön aikana mu
Page 49 and 50: 44Diagnostiikka voi sisältää my
Page 51 and 52: 46tustehtäviin. Tunnistus ymmärt
Page 53 and 54: 48Kirjastosta on saatavilla myös k
Page 55 and 56: 50WindFORM OCR ei sisällä oletuks
Page 57 and 58: 52tomatiikan olleessa käytössä r
Page 59 and 60: 54[Beitzel et al., 2003] Steven M.
Page 61: 56[Klink et al., 2000] Stefan Klink
Page 65 and 66: 60[Tang and Liu, 1997] Yuan Yan Tan
Page 67 and 68: 62Liite 1OCR-kirjastojen käyttäm
Page 69: 64Liite 2OCR-verkkopalvelujen käyt
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