Hidden Markov Models, HMM

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,
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
Modelling Polyfont Printed Characters with HMMS and a Shift Invariant Hamming Distance,
ICDAR95(504-507). BibRef
Combination HMMs for the Recognition of Noisy Printed Characters,
PDF Version. 9409

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

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

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,
IEEE Top Reference. 0209
Automatic segmentation of the IAM off-line database for handwritten English text,
ICPR02(IV: 35-39).
IEEE DOI Link 0211

Anigbogu, J.C., Belaid, A.,
Performance evaluation of an HMM based OCR system,
IEEE DOI Link 9208

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:Nov 25, 2014 at 19:37:27