23.4.2.5 Hidden Markov Models, HMM

Chapter Contents (Back)
OCR. HMM. 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 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.
Elsevier DOI 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 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 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 File. 9409
BibRef

Kim, H.J.[Hang Joon], Kim, S.K.[Sang Kyoon], Kim, K.H.[Kyung Hyun], Lee, J.K.[Jong Kook],
An HMM-Based Character-Recognition Network Using Level Building,
PR(30), No. 3, March 1997, pp. 491-502.
Elsevier DOI 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 BibRef 9908


Fujii, Y.[Yasuhisa], Genzel, D.[Dmitriy], Popat, A.C.[Ashok C.], Teunen, R.[Remco],
Label transition and selection pruning and automatic decoding parameter optimization for time-synchronous Viterbi decoding,
ICDAR15(756-760)
IEEE DOI 1511
HMM for documents. BibRef

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 0211
BibRef

Anigbogu, J.C., Belaid, A.,
Performance evaluation of an HMM based OCR system,
ICPR92(II:565-568).
IEEE DOI 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:Aug 16, 2018 at 18:22:30