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Chapter
Contents (Back)
OCR. Character Recognition.
Vlontzos, J.A., Kung, S.Y.,
Hidden Markov Models For Character Recognition,
IP(1),
No. 4, October 1992, pp. 539-543.
IEEE DOI Link BibRef
9210
Elms, A.J., Illingworth, J.,
Combination of HMMs for the Representation of
Printed Characters
in Noisy Document Images,
IVC(13),
No. 5, June 1995, pp. 385-392.
WWW
Version. BibRef
9506
Elms, A.J., Illingworth, J., and Procter, S.,
The Advantage of Using an HMM-based Approach for
Faxed Word Recognition,
IJDAR(1),
No. 1, Spring 1998, pp. xx-yy. BibRef
9800
Elms, A.J.[Andrew J.],
The Representation and Recognition of Text
Using Hidden Markov Models,
Ph.D.Thesis,
University of Surrey, 1996.
HTML
Version. BibRef
9600
Elms, A.J., Procter, S.[Steve], Illingworth, J.[John],
The recognition of handwritten digit strings of
unknown length using
hidden Markov models,
ICPR98(Vol
II: 1515-1517).
IEEE DOI Link 9808
Variable-Depth Level Building for HMM-Based Recognition of
Handwritten Text BibRef
Elms, A.J., Illingworth, J.,
A Hidden Markov Model Approach for Degraded and
Connected Character Recognition: A European Perspective,
IEE
Digest(123), No. 8, 1994, pp. 1-7. BibRef
9400
Elms, A.J.,
A Connected Character Recogniser Using Level
Building of HMMS,
ICPR94(B:439-441).
IEEE DOI Link BibRef
9400
Elms, A.J., Illingworth, J.,
The Recognition of Noise Polyfont Printed Text
Using Combined HMMS,
SDAIR95(203-216).
BibRef
9500
Earlier:
Modelling Polyfont Printed Characters with HMMS
and a
Shift Invariant Hamming Distance,
ICDAR95(504-507).
BibRef
Earlier:
Combination HMMs for the Recognition of Noisy
Printed Characters,
BMVC94(185-194).
PDF Version.
9409
BibRef
Kim, H.J., Kim, S.K., Kim, K.H., Lee, J.K.,
An HMM-Based Character-Recognition Network Using
Level Building,
PR(30),
No. 3, March 1997, pp. 491-502.
WWW
Version. 9705
BibRef
Schenkel, M., Jabri, M.,
Low-Resolution, Degraded Document Recognition
Using
Neural Networks and Hidden Markov Models,
PRL(19),
No. 3-4, March 1998, pp. 365-371.
9807
BibRef
Yen, C., Kuo, S., Lee, C.H.,
Minimum Error Rate Training for PHMM-Based Text
Recognition,
IP(8),
No. 8, August 1999, pp. 1120-1124.
IEEE DOI Link BibRef
9908
Zimmermann, M., Bunke, H.,
Hidden markov model length optimization for
handwriting recognition
systems,
FHR02(369-374).
IEEE
Top Reference. 0209
BibRef
Earlier:
Automatic segmentation of the IAM off-line
database for handwritten
English text,
ICPR02(IV:
35-39).
IEEE DOI Link
0211
BibRef
Anigbogu, J.C., Belaid, A.,
Performance evaluation of an HMM based OCR system,
ICPR92(II:565-568).
IEEE DOI Link 9208
BibRef
Ma, Y.L.,
Pattern Recognition by Markovian Dynamic
Programming,
ICPR84(1259-1262).
BibRef
8400
Chapter on OCR, Document Analysis and Character Recognition Systems
continues in
Character Segmentation, Segmentation of Individual Characters .
Last update:Jul 26, 2009 at 18:46:29
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