25.4.6.5.1 Multiple Classifiers Applied to Arabic Numbers

Duerr, B., Haettich, W., Tropf, H., and Winkler, G.,
A Combination of Statistical and Syntactical Pattern Recognition Applied to Classification of Unconstrained Handwritten Numerals,
PR(12), No. 3, 1980, pp. 189-199.
Elsevier DOI BibRef 8000

Kimura, M., and Sridhar, M.,
Handwritten Numerical Recognition Based on Multiple Algorithms,
PR(24), No. 10, 1991, pp. 969-983.
Elsevier DOI Utilize heuristic decision rules for combinations. BibRef 9100

Huang, Y.S., Suen, C.Y.,
Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals,
PAMI(17), No. 1, January 1995, pp. 90-94.
IEEE DOI BibRef 9501
Earlier:
A Method of Combining Multiple Classifiers: A Neural Network Approach,
ICPR94(B:473-475).
IEEE DOI 9410
Multistage Classification. BibRef

Kim, K.K.[Kye Kyung], Chung, Y.K.[Yun Koo], Kim, J.H.[Jin Ho], Suen, C.Y.,
Recognition of unconstrained handwritten numeral strings using decision value generator,
ICDAR01(14-17).
IEEE DOI 0109
BibRef

Kim, K.K.[Kye Kyung], Kim, J.H.[Jin Ho], Suen, C.Y.,
Recognition of Unconstrained Handwritten Numeral Strings by Composite Segmentation Method,
ICPR00(Vol II: 594-597).
IEEE DOI 0009
BibRef

Sadri, J.[Javad], Suen, C.Y.[Ching Y.], Bui, T.D.[Tien D.],
A genetic framework using contextual knowledge for segmentation and recognition of handwritten numeral strings,
PR(40), No. 3, March 2007, pp. 898-919.
Elsevier DOI 0611
Genetic algorithm; Contextual knowledge BibRef

Oliveira, L.S., Sabourin, R., Bortolozzi, F., Suen, C.Y.,
Impacts of verification on a numeral string recognition system,
PRL(24), No. 7, April 2003, pp. 1023-1031.
Elsevier DOI 0301

See also Feature selection for ensembles applied to handwriting recognition. BibRef

Oliveira, L.S.[Luiz S.], Sabourin, R.[Robert], Bortolozzi, F.[Flávio], Suen, C.Y.[Ching Y.],
Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy,
PAMI(24), No. 11, November 2002, pp. 1438-1454.
IEEE Abstract. 0211
BibRef
Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Digit Recognition,
ICPR02(I: 568-571).
IEEE DOI 0211
BibRef
Earlier:
A modular system to recognize numerical amounts on Brazilian bank cheques,
ICDAR01(389-394).
IEEE DOI 0109

See also Unsupervised Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Word Recognition. Segmentation based recognition. Combine results of segemntation, recognition and postprocessing. BibRef

Morita, M., Sabourin, R., Bortolozzi, F., Suen, C.Y.,
A recognition and verification strategy for handwritten word recognition,
ICDAR03(482-486).
IEEE DOI 0311
BibRef

de Souza Britto, Jr., A.[Alceu], Sabourin, R.[Robert], Bortolozzi, F.[Flavio], Suen, C.Y.[Ching Y.],
The recognition of handwritten numeral strings using a two-stage HMM-based method,
IJDAR(5), No. 2-3, April 2003, pp. 102-117.
Springer DOI 0308
BibRef
Earlier:
Complementary features combined in an HMM-based system to recognize handwritten digits,
CIAP03(670-675).
IEEE DOI 0310
BibRef
Earlier:
A Two-Stage HMM-Based System for Recognizing Handwritten Numeral Strings,
ICDAR01(396-400).
IEEE DOI 0109
BibRef

Oliveira, L.S., de Souza Britto, Jr., A.[Alceu], Sabourin, R.,
Improving cascading classifiers with particle swarm optimization,
ICDAR05(II: 570-574).
IEEE DOI 0508
BibRef

Zhou, J.[Jun], Suen, C.Y., Liu, K.[Ke],
A feedback-based approach for segmenting handwritten legal amounts on bank cheques,
ICDAR01(887-891).
IEEE DOI 0109
BibRef

Cao, J., Ahmadi, M., Shridhar, M.,
Recognition of Handwritten Numerals with Multiple Feature and Multistage Classifier,
PR(28), No. 2, February 1995, pp. 153-160.
Elsevier DOI BibRef 9502

Beiraghi, S., Ahmadi, M., Shridhar, M., Sid-Ahmed, M.,
Application of Fuzzy Integrals in Fusion of Classifiers for Low Error Rate Handwritten Numerals Recognition,
ICPR00(Vol II: 487-490).
IEEE DOI 0009
BibRef

Liu, C.L., Kim, J.H., Dai, R.W.,
Multiresolution Locally Expanded Honn for Handwritten Numeral Recognition,
PRL(18), No. 10, October 1997, pp. 1019-1025. 9802
BibRef

Rahman, A.F.R., Fairhurst, M.C.,
A New Hybrid Approach in Combining Multiple Experts to Recognize Handwritten Numerals,
PRL(18), No. 8, August 1997, pp. 781-790. 9801
BibRef
And:
A novel pair-wise recognition scheme for handwritten characters in the framework of a multi-expert configuration,
CIAP97(II: 624-631).
Springer DOI 9709
BibRef

Rahman, A.F.R., Fairhurst, M.C.,
Introducing new multiple expert decision combination topologies: a case study using recognition of handwritten characters,
ICDAR97(886-891).
IEEE DOI 9708
BibRef

Rahman, A.F.R., Fairhurst, M.C.,
An Evaluation of Multiexpert Configurations for the Recognition of Handwritten Numerals,
PR(31), No. 9, September 1998, pp. 1255-1273.
Elsevier DOI Evaluation, OCR. 9808

See also Design Considerations in the Real-Time Implementation of Multiple Expert Image Classifiers within a Modular and Flexible Multiple-platform Design Environment. BibRef

Cai, J.H.[Jin-Hai], Liu, Z.Q.[Zhi-Qiang],
Integration of Structural and Statistical Information for Unconstrained Handwritten Numeral Recognition,
PAMI(21), No. 3, March 1999, pp. 263-270.
IEEE DOI BibRef 9903
Earlier: ICPR98(Vol I: 378-380).
IEEE DOI 9808
BibRef

Salah, A.A., Alpaydin, E., Akarun, L.,
A Selective Attention-Based Method for Visual Pattern Recognition with Application to Handwritten Digit Recognition and Face Recognition,
PAMI(24), No. 3, March 2002, pp. 420-425.
IEEE DOI 0202
BibRef

Alimoglu, F., Alpaydin, E.,
Combining Multiple Representations and Classifiers for Pen-Based Handwritten Digit Recognition,
ICDAR97(637-640).
IEEE DOI 9708
BibRef

Kang, H.J.[Hee-Joong],
Combining multiple classifiers based on third-order dependency for handwritten numeral recognition,
PRL(24), No. 16, December 2003, pp. 3027-3036.
Elsevier DOI 0310
BibRef

Kang, H.J.[Hee-Joong], Doermann, D.S.,
Combining multiple classifiers based on third-order dependency,
ICDAR03(21-25).
IEEE DOI 0311
BibRef

Kang, H.J.[Hee-Joong], Doermann, D.S.,
Selection of classifiers for the construction of multiple classifier systems,
ICDAR05(II: 1194-1198).
IEEE DOI 0508
BibRef
Earlier:
Product Approximation by Minimizing the Upper Bound of Bayes Error Rate for Bayesian Combination of Classifiers,
ICPR04(I: 252-255).
IEEE DOI 0409
BibRef
Earlier:
Evaluation of the information-theoretic construction of multiple classifier systems,
ICDAR03(789-793).
IEEE DOI 0311
BibRef

Kang, H.J.[Hee-Joong], Lee, S.W.[Seong-Whan],
Evaluation on selection criteria of multiple numeral recognizers with the fixed number of recognizers,
ICPR02(III: 403-406).
IEEE DOI 0211
BibRef
Earlier:
Experimental results on the construction of multiple classifiers recognizing handwritten numerals,
ICDAR01(1026-1030).
IEEE DOI 0109
BibRef
Earlier:
An Information-theoretic Strategy for Constructing Multiple Classifier Systems,
ICPR00(Vol II: 483-486).
IEEE DOI 0009
BibRef
Earlier:
A Dependency-based Framework of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals,
CVPR99(II: 124-129).
IEEE DOI BibRef

Suen, C.Y.[Ching Y.], Tan, J.[Jinna],
Analysis of errors of handwritten digits made by a multitude of classifiers,
PRL(26), No. 3, February 2005, pp. 369-379.
Elsevier DOI 0501
BibRef

Silva, T.C.[Thiago C.], Zhao, L.[Liang], Cupertino, T.H.[Thiago H.],
Handwritten Data Clustering Using Agents Competition in Networks,
JMIV(45), No. 3, March 2013, pp. 264-276.
Springer DOI 1301
Learning on networks. Solve handwritten digit recognition. BibRef

Mozafari, M.[Milad], Ganjtabesh, M.[Mohammad], Nowzari-Dalini, A.[Abbas], Thorpe, S.J.[Simon J.], Masquelier, T.[Timothée],
Bio-inspired digit recognition using reward-modulated spike-timing-dependent plasticity in deep convolutional networks,
PR(94), 2019, pp. 87-95.
Elsevier DOI 1906
Spiking neural networks, Deep architecture, Digit recognition, STDP, Reward-modulated STDP, Latency coding BibRef

Jiang, W.W.[Wei-Wei], Zhang, L.[Le],
Edge-SiamNet and Edge-TripleNet: New Deep Learning Models for Handwritten Numeral Recognition,
IEICE(E103-D), No. 3, March 2020, pp. 720-723.
WWW Link. 2003
BibRef

Mukarambi, G.[Gururaj], Dhandra, B.V.,
Energy-based features for Kannada handwritten digit recognition,
IJCVR(10), No. 2, 2020, pp. 156-166.
DOI Link 2003
BibRef

Zhan, H.J.[Hong-Jian], Lyu, S.[Shujing], Lu, Y.[Yue], Pal, U.[Umapada],
DenseNet-CTC: An end-to-end RNN-free architecture for context-free string recognition,
CVIU(204), 2021, pp. 103168.
Elsevier DOI 2102
Handwritten digit string recognition, End-to-end, RNN-free, Connectionist temporal classification BibRef

Rababaah, A.R.[Aaron Rasheed],
A comparative study between convolution neural networks and multi-layer perceptron networks for hand-written digits recognition,
IJCVR(13), No. 4, 2023, pp. 420-436.
DOI Link 2307
BibRef

Kenia, R.[Roshan], Mendil, J.[Jihane], Jasim, A.[Ahmed], Al-Dahhan, M.[Muthanna], Yin, Z.Z.[Zhao-Zheng],
Robust TRISO-fueled Pebble Identification by Digit Recognition,
WACV24(8142-8150)
IEEE DOI 2404
Real-time systems, Climate change, Visualization, Nuclear power generation, Inductors, Visualization BibRef

Rim, P.[Patrick], Saha, S.[Snigdha], Rim, M.[Marcus],
Caltechfn: Distorted and Partially Occluded Digits,
MLCSA22(195-212).
Springer DOI 2307
BibRef

Guo, J.[Jun], Wei, W.J.[Wen-Jing], Ma, Y.F.[Yi-Feng], Peng, C.[Cong],
A Fast and Accurate Object Detector for Handwritten Digit String Recognition,
ICPR21(787-794)
IEEE DOI 2105
Handwriting recognition, Image resolution, Databases, Target recognition, Graphics processing units, Detectors, Encoding BibRef

Chi, D.,
Handwritten Digit Recognition Application Based on Improved Naive Bayes Method,
CVIDL20(622-624)
IEEE DOI 2102
Bayes methods, handwritten character recognition, image classification, improved naive Bayes method, classification BibRef

Zhan, H., Lyu, S., Lu, Y.,
Handwritten Digit String Recognition using Convolutional Neural Network,
ICPR18(3729-3734)
IEEE DOI 1812
Feature extraction, Training, Recurrent neural networks, Text recognition, Task analysis, Computer architecture, Testing BibRef

Meier, U.[Ueli], Ciresan, D.C.[Dan Claudiu], Gambardella, L.M.[Luca Maria], Schmidhuber, J.[Jurgen],
Better Digit Recognition with a Committee of Simple Neural Nets,
ICDAR11(1250-1254).
IEEE DOI 1111
BibRef

Štrba, M.[Miroslav], Herout, A.[Adam], Havel, J.[Jirí],
Handwritten Digits Recognition Improved by Multiresolution Classifier Fusion,
IbPRIA11(726-733).
Springer DOI 1106
BibRef

Boto, F.[Fernando], Cortés, A.[Andoni], Rodríguez, C.[Clemente],
Cut Digits Classification with k-NN Multi-specialist,
DAS06(496-505).
Springer DOI 0602
BibRef

Gupta, M.D.[Mithun Das], Rajaram, S.[Shyamsundar], Petrovic, N.[Nemanja], Huang, T.S.[Thomas S.],
Restoration and Recognition in a Loop,
CVPR05(I: 638-644).
IEEE DOI 0507
Restoration of blurred digits with recognition. BibRef

Tomai, C.I., Srihari, S.N.,
Combination of type III digit recognizers using the Dempster-Shafer theory of evidence,
ICDAR03(854-858).
IEEE DOI 0311
BibRef

Bhattacharya, U., Chaudhuri, B.B.,
A majority voting scheme for multiresolution recognition of handprinted numerals,
ICDAR03(16-20).
IEEE DOI 0311
BibRef

Rodriguez, C., Boto, F., Soraluze, I., Perez, A.,
An incremental and hierarchical K-NN classifier for handwritten characters,
ICPR02(III: 98-101).
IEEE DOI 0211
BibRef

Soraluze, I., Rodriguez, C., Boto, F., Perez, A.,
Multidimensional multistage K-NN classifiers for handwritten digit recognition,
FHR02(19-23).
IEEE Top Reference. 0209
BibRef

Rodriguez, C., Muguerza, J.[Javier], Navarro, M., Zarate, A., Martin, J.I.[Jose Ignacio], Perez, J.,
A Two-Stage Classifier for Broken and Blurred Digits in Forms,
ICPR98(Vol II: 1101-1105).
IEEE DOI 9808
BibRef

Teo, R.Y.M., Shinghal, R.,
A Hybrid Classifier for Recognizing Handwritten Numerals,
ICDAR97(283-287).
IEEE DOI 9708
BibRef

Kim, J., Seo, K., Chung, K.,
A Systematic Approach to Classifier Selection on Combining Multiple Classifiers for Handwritten Digit Recognition,
ICDAR97(459-462).
IEEE DOI 9708
BibRef

Bottou, L., Cortes, C., Denker, J.S., Drucker, H., Guyon, I., Jackel, L.D., Le Cun, Y.L., Muller, U.A., Sackinger, E., Simard, P.Y., Vapnik, V.,
Comparison of classifier methods: a case study in handwritten digit recognition,
ICPR94(B:77-82).
IEEE DOI 9410
BibRef

Simard, P.Y., Le Cun, Y.L.[Yann L.], Denker, J.S.,
Memory-based character recognition using a transformation invariant metric,
ICPR94(B:262-267).
IEEE DOI 9410
BibRef

Sabourin, M., Mitiche, A., Thomas, D., Nagy, G.,
Classifier Combination for Handprinted Digit Recognition,
ICDAR93(163-166). BibRef 9300

Chapter on OCR, Document Analysis and Character Recognition Systems continues in
Numbers, Digits, Zip (Postal) Codes .