23.4.6.5.3 Neural Networks for Numbers and Digits

Chapter Contents (Back)
OCR. Character Recognition. Numbers. Neural Networks.

Gader, P.D., Khabou, M.A.,
Automatic Feature Generation for Handwritten Digit Recognition,
PAMI(18), No. 12, December 1996, pp. 1256-1261.
IEEE DOI Link 9701
Neural Networks. Two different measures, orthogonality and information, guide the search for features to use in the NN implementation. Uses multiple classifiers. Recognition rates are around 98% on a test set with 1000 digits per class. BibRef

Chiang, J.H., Gader, P.D.,
A Hybrid Feature Extraction Framework for Handwritten Numeric Fields Recognition,
ICPR96(IV: 436-440).
IEEE DOI Link 9608
(Ind. Tech. Res. Institute, ROC) BibRef

Knerr, S., Personnaz, L., and Dreyfus, G.,
Handwritten Digit Recognition by Neural Networks with Single-Layer Training,
TNN(3), No. 6, November 1992, pp. 962-968. BibRef 9211

Calabro, C., Ferragina, P., Granieri, M.N.,
Recognition of Hand-Written Rotated Digits by Neural Networks,
MVA(8), No. 5, 1995, pp. 351-357.
HTML Version. BibRef 9500

Lee, S.W.[Seong-Whan],
Off-Line Recognition of Totally Unconstrained Handwritten Numerals Using Multilayer Cluster Neural-Network,
PAMI(18), No. 6, June 1996, pp. 648-652.
IEEE DOI Link 9607
BibRef
Earlier: Add Kim, Y.J.[Young Joon], : ICPR94(B:507-509).
IEEE DOI Link 9410
BibRef

Lee, S.W., Kim, C.H., Ma, H., Tang, Y.Y.,
Multiresolution Recognition of Unconstrained Handwritten Numerals with Wavelet Transform and Multilayer Cluster Neural-Network,
PR(29), No. 12, December 1996, pp. 1953-1961.
WWW Version. 9701
Wavelets. Neural Networks. BibRef

Cho, S.B.,
Neural-Network Classifiers for Recognizing Totally Unconstrained Handwritten Numerals,
TNN(8), No. 1, January 1997, pp. 43-53. 9701
BibRef
Earlier:
Recognition Of Unconstrained Handwritten Numerals by Doubly Self-Organizing Neural Network,
ICPR96(IV: 426-430).
IEEE DOI Link 9608
(Yonsei Univ., KOR) BibRef

Cao, J., Ahmadi, M., Shridhar, M.,
A Hierarchical Neural-Network Architecture for Handwritten Numeral Recognition,
PR(30), No. 2, February 1997, pp. 289-294.
WWW Version. 9704
BibRef

Cao, J., Shridhar, M., Kimura, F., Ahmadi, M.,
Statistical and neural classification of handwritten numerals: a comparative study,
ICPR92(II:643-646).
IEEE DOI Link 9208
BibRef

Reddy, N.V.S., Nagabhushan, P.,
A 3-Dimensional Neural-Network Model for Unconstrained Handwritten Numeral Recognition: A New Approach,
PR(31), No. 5, May 1998, pp. 511-516.
WWW Version. 9805
BibRef

Hwang, Y.S.[Young-Sup], Bang, S.Y.[Sung-Yang],
Recognition of Unconstrained Handwritten Numerals by a Radial Basis Function Neural Network Classifier,
PRL(18), No. 7, July 1997, pp. 657-664. 9711
BibRef
Earlier:
An Efficient Method to Construct a Radial Basis Function Neural Network Classifier and its Application to Unconstrained Handwritten Digit Recognition,
ICPR96(IV: 640-644).
IEEE DOI Link 9608
(Pohang Institute of Science, KOR) BibRef

Lee, S.W., Kim, S.Y.,
Integrated Segmentation and Recognition of Handwritten Numerals with Cascade Neural Network,
SMC-C(29), No. 2, May 1999, pp. 285.
IEEE Top Reference. BibRef 9905

Zhou, J.[Jie], Gan, Q.A.[Qi-Ang], Krzyzak, A.[Adam], Suen, C.Y.[Ching Y.],
Recognition of handwritten numerals by Quantum Neural Network with fuzzy features,
IJDAR(2), No. 1, 1999, pp. 30-36. BibRef 9900

Wang, J.G.[Jian-Guo], Yan, H.[Hong],
A hybrid method for unconstrained handwritten numeral recognition by combining structural and neural 'gas' classifiers,
PRL(21), No. 6-7, June 2000, pp. 625-635. 0006
See also Model-Based Segmentation Method for Handwritten Numeral Strings, A. BibRef

Zhang, B.L.[Bai-Ling], Fu, M.Y.[Min-Yue], Yan, H.[Hong],
A nonlinear neural network model of mixture of local principal component analysis: application to handwritten digits recognition,
PR(34), No. 2, February 2001, pp. 203-214.
WWW Version. 0011
BibRef

Zhang, B.L.[Bai-Ling], Fu, M.Y.[Min-Yue], Yan, H.[Hong],
A Modular Classification Scheme with Elastic Net Models for Handwritten Digit Recognition,
ICPR98(Vol II: 1859-1861).
IEEE DOI Link 9808
BibRef


You, D.K.[Dae-Keun], Kim, G.H.[Gyeong-Hwan],
An approach for locating segmentation points of handwritten digit strings using a neural network,
ICDAR03(142-146).
IEEE Abstract. 0311
BibRef

Wong, K.W.[Kwok-Wo], Leung, C.S.[Chi-Sing], Chang, S.J.[Sheng-Jiang],
Handwritten digit recognition using multi-layer feedforward neural networks with periodic and monotonic activation functions,
ICPR02(III: 106-109).
IEEE DOI Link 0211
BibRef

Kim, H.Y.[Ho-Yon], Lim, K.T.[Kil-Taek], Nam, Y.S.[Yun-Seok],
Handwritten numeral string recognition using neural network classifier trained with negative data,
FHR02(395-400).
IEEE Top Reference. 0209
BibRef

Lim, K.T.[Kil-Taek], Chien, S.I.[Sung-Il],
Neural Network Based Feature Space Generation for Multiple Databases of Handwritten Numerals,
ICPR98(Vol I: 375-377).
IEEE DOI Link 9808
BibRef

Kawaguchi, T., Nagata, R.I., Miyake, Y.[Yasuji], Inoue, S., Wakabayashi, T.[Tetsushi], Kimura, F.[Fumitaka], Tsuruoka, S.,
Handwritten Numeral Recognition Using Autoassociative Neural Networks,
ICPR98(Vol I: 166-171).
IEEE DOI Link 9808
BibRef

Lin, X., Ding, X., Wu, Y.,
Handwritten Numeral Recognition Using MFNN Based Multiexpert Combination Strategy,
ICDAR97(471-474).
IEEE DOI Link 9708
BibRef

Lee, S., and Pan, J.C.,
Handwritten Numeral Recognition Based on Hierarchically Self-Organizing Learning Networks with Spatio-Temporal Pattern Recognition,
CVPR92(176-182).
IEEE DOI Link BibRef 9200

Williams, C.K.I.,
Combining Deformable Models and Neural Networks for Handprinted Digit Recognition,
Ph.D.Thesis, 1994, BibRef 9400 Toronto BibRef

Lemarie, B.,
Practical Implementation of a Radial Basis Function Network for Handwritten Digit Recognition,
ICDAR93(412-415). BibRef 9300

Tsay, S.C.[Shuh-Chuan], Hong, P.R.[Peir-Ren], Chieu, B.C.[Bin-Chang],
Handwritten digits recognition system via OCON neural network by pruning selective update,
ICPR92(II:656-659).
IEEE DOI Link 9208
BibRef

Chapter on OCR, Document Analysis and Character Recognition Systems continues in
Money and Check Processing -- Amounts, etc. .


Last update:Oct 15, 2014 at 21:10:33