25.4.6.5.2 Numbers, Digits, Zip (Postal) Codes

Chapter Contents (Back)
OCR. Zip Codes. Postal Codes.
See also Mail -- Addresses, Document Analysis, Postal Automation.

Ullmann, J.R., Kidd, P.A.,
Recognition experiments with typed numerals from envelopes in the mail,
PR(1), No. 4, July 1969, pp. 273-289.
Elsevier DOI 0309

See also Parallel Recognition of Idealised Line Characters. BibRef

Mitchell, B.T., and Gillies, A.M.,
A Model-Based Computer Vision System for Recognizing Handwritten ZIP Codes,
MVA(2), No. 4, 1989, pp. 231-243. BibRef 8900

Ahmed, P., Suen, C.Y.,
Computer Recognition of Totally Unconstrained Handwritten Zip Codes,
PRAI(1), No. 1, 1987, pp. 1-15. BibRef 8700

Lam, L., Suen, C.Y.,
Structural Classification and Relaxation Matching of Totally Unconstrained Handwritten Zip-Code Numbers,
PR(21), No. 1, 1988, pp. 19-31.
Elsevier DOI BibRef 8800

Mai, T., Suen, C.Y.,
A Generalized Knowledge-Based System for the Recognition of Unconstrained Handwritten Numerals,
SMC(20), No. 4, 1990, pp. 835-848. BibRef 9000

Le Cun, Y.L., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.,
Back-Propagation Applied to Handwritten Zip Code Recognition,
NeurComp(1), 1989, pp. 541-551. BibRef 8900

Le Cun, Y.L., Jackel, L.D., Boser, B., Denker, J.S.,
Handwritten Digit Recognition: Application of Neural Network Chips and Automatic Learning,
CommunMag(27), No. 11, November 1989, pp. 41-46. BibRef 8911

Le Cun, Y.L., Matan, O., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jacket, L.D., Baird, H.S.,
Handwritten zip code recognition with multilayer networks,
ICPR90(II: 35-40).
IEEE DOI 9208
BibRef

Cesar, M., and Shinghal, R.,
Algorithm for Segmenting Handwritten Postal Codes,
MMS(33), No. 1, July 1990, pp. 63-80. BibRef 9007

Bouchaffra, D.[Djamel], Govindaraju, V.[Venu], Srihari, S.N.[Sargur N.],
Postprocessing of Recognized Strings Using Nonstationary Markovian Models,
PAMI(21), No. 10, October 1999, pp. 990-999.
IEEE DOI For digits, Zip Codes. BibRef 9910

Bouchaffra, D.[Djamel], Govindaraju, V.[Venu], Srihari, S.N.[Sargur N.],
Recognition of Strings Using Non-stationary Markovian Models: An Application to ZIP Code Recognition,
CVPR99(II: 174-179).
IEEE DOI BibRef 9900

Kussul, E.[Ernst], Baidyk, T.[Tatiana],
Improved Method of Handwritten Digit Recognition Tested on MNIST Database,
IVC(22), No. 12, 1 October 2004, pp. 971-981.
Elsevier DOI 0409
BibRef
Earlier: VI02(192).
PDF File. 0208
BibRef

Liu, C.L.[Cheng-Lin], Nakashima, K.[Kazuki], Sako, H.[Hiroshi], Fujisawa, H.[Hiromichi],
Handwritten Digit Recognition: Benchmarking of State-of-the-Art Techniques,
PR(36), No. 10, October 2003, pp. 2271-2285.
Elsevier DOI 0308
Survey, Digit Recognition. BibRef
Earlier:
Handwritten digit recognition using state-of-the-art techniques,
FHR02(320-325).
IEEE Top Reference. 0209
BibRef

Liu, C.L.[Cheng-Lin], Nakashima, K.[Kazuki], Sako, H.[Hiroshi], Fujisawa, H.[Hiromichi],
Handwritten Digit Recognition: Investigation of Normalization and Feature Extraction Techniques,
PR(37), No. 2, February 2004, pp. 265-279.
Elsevier DOI 0311
BibRef

Liu, C.L., Koga, M., Fujisawa, H.,
Gabor feature extraction for character recognition: Comparison with gradient feature,
ICDAR05(I: 121-125).
IEEE DOI 0508
BibRef

Liu, C.L.[Cheng-Lin],
Normalization-Cooperated Gradient Feature Extraction for Handwritten Character Recognition,
PAMI(29), No. 8, August 2007, pp. 1465-1469.
IEEE DOI 0707
BibRef

Liu, C.L.[Cheng-Lin], Mine, R., Koga, M.,
Building Compact Classifier for Large Character Set Recognition Using Discriminative Feature Extraction,
ICDAR05(II: 846-850).
IEEE DOI 0508

See also Normalization-Cooperated Gradient Feature Extraction for Handwritten Character Recognition. BibRef

Liu, C.L.[Cheng-Lin], Sako, H., Fujisawa, H.,
Effects of Classifier Structures and Training Regimes on Integrated Segmentation and Recognition of Handwritten Numeral Strings,
PAMI(26), No. 11, November 2004, pp. 1395-1407.
IEEE Abstract. 0410
Include non-character training. SVM gave best results, but higher cost than neural and discriminative density models. BibRef

Liu, C.L.[Cheng-Lin],
One-Vs-All Training of Prototype Classifier for Pattern Classification and Retrieval,
ICPR10(3328-3331).
IEEE DOI 1008
BibRef

Liu, C.L.[Cheng-Lin], Sako, H.[Hiroshi],
Class-specific feature polynomial classifier for pattern classification and its application to handwritten numeral recognition,
PR(39), No. 4, April 2006, pp. 669-681.
Elsevier DOI 0604
Neural classifiers; Class-specific feature polynomial classifier; Numeral string recognition BibRef

Liu, C.L.[Cheng-Lin], Marukawa, K.,
Handwritten Numeral String Recognition: Character-Level vs. String-Level Classifier Training,
ICPR04(I: 405-408).
IEEE DOI 0409
BibRef

Koch, G., Heutte, L.[Laurent], Paquet, T.[Thierry],
Automatic extraction of numerical sequences in handwritten incoming mail documents,
PRL(26), No. 8, June 2005, pp. 1118-1127.
Elsevier DOI 0506
BibRef
Earlier:
Numerical sequence extraction in handwritten incoming mail documents,
ICDAR03(369-373).
IEEE DOI 0311
BibRef

Thomas, S.[Simon], Chatelain, C.[Clement], Heutte, L.[Laurent], Paquet, T.[Thierry],
Alpha-Numerical Sequences Extraction in Handwritten Documents,
FHR10(232-237).
IEEE DOI 1011
BibRef

USPS Office of Advanced Technology Database of Handwritten Cities, States, ZIP Codes, Digits, and Alphabetic Characters,
Cedar (Buffalo) database. Dataset, Handwriting.
WWW Link. Database for mail processing.

Basu, S.[Subhadip], Das, N.[Nibaran], Sarkar, R.[Ram], Kundu, M.[Mahantapas], Nasipuri, M.[Mita], Basu, D.K.[Dipak Kumar],
A novel framework for automatic sorting of postal documents with multi-script address blocks,
PR(43), No. 10, October 2010, pp. 3507-3521.
Elsevier DOI 1007
BibRef
Earlier:
Recognition of Numeric Postal Codes from Multi-script Postal Address Blocks,
PReMI09(381-386).
Springer DOI 0912
Automatic mail sorting; Multi-script postal address block; Script identification for numerals; Quad-tree based feature extraction; Handwritten numeral recognition; Support vector machine BibRef


Sharma, N., Sengupta, A., Sharma, R., Pal, U., Blumenstein, M.,
Pincode detection using deep CNN for postal automation,
IVCNZ17(1-6)
IEEE DOI 1902
convolution, document image processing, feedforward neural nets, handwritten character recognition, postal services, Feature extraction BibRef

Park, S.W.[Sung-Won], Yang, Y.H.[Yun-Ho], Kim, G.H.[Gyeong-Hwan], Jeong, S.H.[Seon-Hwa],
A two-stage approach for segmentation and recognition of handwritten digit strings collected from mail pieces,
ICPR04(II: 626-629).
IEEE DOI 0409
BibRef

Dzuba, G., Filatov, A., Volgunin, A.,
Handwritten Zip Code Recognition,
ICDAR97(766-770).
IEEE DOI 9708
BibRef

Lecolinet, E., and Moreau, J.V.,
A New System for Automatic Segmentation and Recognition of Unconstrained Zip Codes,
SCIA89(585).
See also Survey of Methods and Strategies in Character Segmentation, A. BibRef 8900

Hull, J.J., Srihari, S.N., Cohen, E., Kuan, L., Cullen, P., Palumbo, P.W.,
A Blackboard-Based Approach to Handwritten Zip Code Recognition,
ICPR88(I: 111-113).
IEEE DOI BibRef 8800

Hull, J.J., Srihari, S.N.,
Knowledge Utilization In Handwritten Zip Code Recognition,
IJCAI87(848-850). BibRef 8700

Chapter on OCR, Document Analysis and Character Recognition Systems continues in
Neural Networks for Numbers and Digits .


Last update:Mar 16, 2024 at 20:36:19