(Summary) Re: Character Recognition Bibliography?
atul k chhabra
Atul.Chhabra at UC.EDU
Sat Jul 21 14:06:31 EDT 1990
Here is a summary of what I received in response to my request for references
on character recognition. I had asked for references in all aspects of
character recognition -- preprocessing and segmentation, OCR, typewritten
character recognition, handwritten character recognition, neural network based
recognition, statistical and syntactic recognition, hardware implementations,
and commercial character recognition systems. THANKS TO ALL WHO RESPONDED.
IF ANYONE OUT THERE HAS MORE REFERENCES, PLEASE EMAIL ME. I WILL SUMMARIZE NEW
RESPONSES AFTER ANOTHER TWO WEEKS. THANKS.
Atul Chhabra
Department of Electrical & Computer Engineering
University of Cincinnati, ML 030
Cincinnati, OH 45221-0030
Phone: (513)556-6297
Email: achhabra at uceng.uc.edu
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From: Sol <sol at iai.es>
Sol Delgado
Instituto de Automatica Industial
La Poveda Arganda del Rey
28500 MADRID
SPAIN
sol at iai.es
[ 1]_ Off-Line cursive script word recognition
Radmilo M. Bozinovic, Sargur N. Srihari
IEEE transactions on pattern analysis and machine intelligence.
Vol. 11, January 1989.
[ 2]_ Visual recognition of script characters. Neural networt architectures.
Jodef Skrzypek, Jeff Hoffman.
MPL (Machine Perception Lab). Nov 1989.
[ 3]_ On recognition of printed characters of any font and size.
Simon Kahan, Theo Pavlidis, Henry S. Baird.
IEEE transactions on pattern analysis and machine intelligence
Vol PAMI_9, No 2, March 1987.
[ 4]_ Research on machine recognition of handprinted characters.
Shunji Mori, Kazuhiko Yamamoto, Michio Yasuda.
IEEE transactions on pattern analysis and machine intelligence.
Vol PAMI_6, No 4. July 1984.
[ 5]_ A pattern description and generation method of structural
characters
Hiroshi nagahashi, Mikio Nakatsuyama.
IEEE transactions on pattern analysis and machine intelligence
Vol PAMI_8, No 1, January 1986.
[ 6]_ An on-line procedure for recognition of handprinted alphanumeric
characters.
W. W. Loy, I. D. Landau.
IEEE transactions on pattern analysis and machine intelligence.
Vol PAMI_4, No 4, July 1982.
[ 7]_ A string correction algorithm for cursive script recognition.
Radmilo Bozinovic, Sargur N. Srihari.
IEEE transactions on pattern analysis and machine inteligence.
Vol PAMI_4, No 6, November 1982.
[ 8]_ Analisys and design of a decision tree based on entropy reduction
and its application to large character set recognition
Qing Ren Wang, Ching Y. Suen.
IEEE transactions on pattern analysis and machine intelligence.
Vol PAMI_6, No 4, July 1984.
[ 9]_ A method for selecting constrained hand-printed character shapes
for machine recognition
Rajjan Shinghal, Ching Y. Suen
IEEE transactions on pattern analysis and machine intelligence.
Vol PAMI_4, No 1, January 1982
[10]_ Pixel classification based on gray level and local "busyness"
Philip A. Dondes, Azriel Rosenfeld.
IEEE transactions on pattern analysis and machine intelligence.
Vol PAMI_4, No 1, January 1982.
[11]_ Experiments in the contextual recognition of cursive script
Roger W. Ehrich, Kenneth J. Koehler
IEEE transactions on computers, vol c-24, No. 2, February 1975.
[12]_ Character recognition by computer and applications.
Ching Y. Suen.
Handbook of pattern recognition and image procesing.
ACADEMIC PRESS, INC. August 1988.
[13]_ A robust algorithm for text string separation from mixed
text/graphics images
Lloyd Alan Fletcher, Rangachar Kasturi
IEEE transactions on pattern analysis and machine intelligence.
Vol 10, No 6, November 1988.
[14]_ Segmentation of document images.
Torfinn Taxt, Patrick J. Flynn, Anil K. Jain
IEEE transactions on pattern analysis and machine intelligence.
Vol 11, No 12, december 1989.
[15]_ Experiments in text recognition with Binary n_Gram and Viterbi
algorithms.
Jonathan J. Hull, Sargur N. Srihari
IEEE transactions on pattern analysis and machine intelligence
Vol PAMI-4, No 5, september 1982.
[16]- Designing a handwriting reader.
D. J. Burr
IEEE transactions on pattern analysis and machine intelligence
Vol PAMI-5, No 5, september 1983.
[17]_ Experiments on neural net recognition of spoken and written text
David J. Burr
IEEE transactions on acoustics, speech and signals processing
vol 36, No 7, july 1988
[18]_ Experimets with a connectionist text reader
D. J. Burr
Bell communications research
Morristow, N. J. 07960
[19]_ An Algorithm for finding a common structure shared by a family of
strings
Anne M. Landraud, Jean-Francois Avril, Philippe Chretienne.
IEEE transactions on pattern analysis and machine intelligence
Vol 11, No 8, august 1989
[20]_ Word_level recognition of cursive script
Raouf F. H. Farag
IEEE transactions on computers
Vol C-28, No 2, february 1979
[21]_ Pattern Classification by neural network: an experimental system
for icon recognition
Eric Gullichsen, Ernest Chang
Marzo, 1987
[22]_ Recognition of handwritten chinese characters by modified hough
transform techniques.
Fang-Hsuan Cheng, Wen-Hsing Hsu, Mei-Ying Chen
IEEE transactions on pattern analysis and machine intelligence
Vol 11, No 4, April 1989
[23]_ Inheret bias and noise in the Hough transform
Christopher M. Brown
IEEE transactions on pattern analysis and machine intelligence
Vol PAMI-5, No 5, september 1983.
[24]_ From pixels to features
J. C. Simon
North-Holland
_ Feature selection and Language syntax in text recognition.
J.J. Hull
_ Feature extraction for locating address blocks on mail pieces.
S.N. Srihari.
[25]_ A model for variability effects in hand-printing, with implications
for the design of on line character recognition systems.
J.R. Ward and T. Kuklinski.
IEEE transactions on systems, man and cybernetics.
Vol 18, No 3, May/June 1988.
[26]_ Selection of a neural network system for visual inspection.
Paul J. Stomski, Jr and Adel S. Elmaghraby
Engineering Mathematics And Computer Science
University of Louisville, Kentucky 40292
[27]_ Self-organizing model for pattern learning and its application to
robot eyesight.
Hisashi Suzuki, Suguru Arimoto.
Proceedings of the fourth conference on A.I.
san Diego, March 1988.
The computer society of the IEEE.
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From: J. Whiteley <WHITELEY-J at OSU-20.IRCC.OHIO-STATE.EDU>
I only have five references I can offer, all are from the Proceedings of the
1989 International Joint Conference on Neural Networks held in Washington D.C.
Yamada, K.
Kami, H.
Tsukumo, J.
Temma, T.
Handwritten Numeral Recognition by Multi-layered Neural Network
with Improved Learning Algorithm
Volume II, pp. 259-266
Morasso, P.
Neural Models of Cursive Script Handwriting
Volume II, pp.539-542
Guyon, I.
Poujaud, I.
Personnaz, L.
Dreyfus, G.
Comparing Different Neural Network Architectures for Classifying
Handwritten Digits
Volume II, pp.127-132
Weideman, W.E.
A Comparison of a Nearest Neighbor Classifier and a Neural Network for
Numeric Handprint Character Recognition
Volume I, pp.117-120
Barnard, E.
Casasent, D.
Image Processing for Image Understanding with Neural Nets
Volume I, pp.111-115
Hopefully you are being deluged with references.
--Rob Whiteley
Dept. of Chemical Engineering
Ohio State University
email: whiteley-j at osu-20.ircc.ohio-state.edu
-------
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From: avi at dgp.toronto.edu (Avi Naiman)
%L Baird 86
%A H. S. Baird
%T Feature Identification for Hybrid Structural/Statistical Pattern Classification
%R Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
%D June 1986
%P 150-155
%L Casey and Jih 83
%A R. G. Casey
%A C. R. Jih
%T A Processor-Based OCR System
%J IBM Journal of Research and Development
%V 27
%N 4
%D July 1983
%P 386-399
%L Cash and Hatamian 87
%A G. L. Cash
%A M. Hatamian
%T Optical Character Recognition by the Method of Moments
%J Computer Vision, Graphics and Image Processing
%V 39
%N 3
%D September 1987
%P 291-310
%L Chanda et al. 84
%A B. Chanda
%A B. B. Chaudhuri
%A D. Dutta Majumder
%T Some Algorithms for Image Enhancement Incorporating Human Visual Response
%J Pattern Recognition
%V 17
%D 1984
%P 423-428
%L Cox et al. 74
%A C. Cox
%A B. Blesser
%A M. Eden
%T The Application of Type Font Analysis to Automatic Character Recognition
%J Proceedings of the Second International Joint Conference on Pattern Recognition
%D 1974
%P 226-232
%L Frutiger 67
%A Adrian Frutiger
%T OCR-B: A Standardized Character for Optical Recognition
%J Journal of Typographic Research
%V 1
%N 2
%D April 1967
%P 137-146
%L Goclawska 88
%A Goclawska
%T Method of Description of the Alphanumeric Printed Characters by Signatures for Automatic Text Readers
%J AMSE Review
%V 7
%N 2
%D 1988
%P 31-34
%L Gonzalez 87
%A Gonzalez
%T Designing Balance into an OCR System
%J Photonics Spectra
%V 21
%N 9
%D September 1987
%P 113-116
%L GSA 84
%A General Services Administration
%T Technology Assessment Report: Speech and Pattern Recognition; Optical Character Recognition; Digital Raster Scanning
%I National Archives and Records Service
%C Washington, District of Columbia
%D October 1984
%L Hull et al. 84
%A J. J. Hull
%A G. Krishnan
%A P. W. Palumbo
%A S. N. Srihari
%T Optical Character Recognition Techniques in Mail Sorting: A Review of Algorithms
%R 214
%I SUNY Buffalo Computer Science
%D June 1984
%L IBM 86
%A IBM
%T Character Recognition Apparatus
%J IBM Technical Disclosure Bulletin
%V 28
%N 9
%D February 1986
%P 3990-3993
%L Kahan et al. 87
%A A. Kahan
%A Theo Pavlidis
%A H. S. Baird
%T On the Recognition of Printed Characters of any Font and Size
%J IEEE Transactions on Pattern Analysis and Machine Intelligence
%V PAMI-9
%N 2
%D March 1987
%P 274-288
%L Lam and Baird 86
%A S. W. Lam
%A H. S. Baird
%T Performance Testing of Mixed-Font, Variable-Size Character Recognizers
%R AT&T Bell Laboratories Computing Science Technical Report No. 126
%C Murray Hill, New Jersey
%D November 1986
%L Lashas et al. 85
%A A. Lashas
%A R. Shurna
%A A. Verikas
%A A. Dosimas
%T Optical Character Recognition Based on Analog Preprocessing and Automatic Feature Extraction
%J Computer Vision, Graphics and Image Processing
%V 32
%N 2
%D November 1985
%P 191-207
%L Mantas 86
%A J. Mantas
%T An Overview of Character Recognition Methodologies
%J Pattern Recognition
%V 19
%N 6
%D 1986
%P 425-430
%L Murphy 74
%A Janet Murphy
%T OCR: Optical Character Recognition
%C Hatfield
%I Hertis
%D 1974
%L Nagy 82
%A G. Nagy
%T Optical Character Recognition \(em Theory and Practice
%B Handbook of Statistics
%E P. R. Krishnaiah and L. N. Kanal
%V 2
%I North-Holland
%C Amsterdam
%D 1982
%P 621-649
%L Pavlidis 86
%A Theo Pavlidis
%T A Vectorizer and Feature Extractor for Document Recognition
%J Computer Vision, Graphics and Image Processing
%V 35
%N 1
%D July 1986
%P 111-127
%L Piegorsch et al. 84
%A W. Piegorsch
%A H. Stark
%A M. Farahani
%T Application of Image Correlation for Optical Character Recognition in Printed Circuit Board Inspection
%R Proceedings of SPIE \(em The International Society for Optical Engineering: Applications of Digital Image Processing VII
%V 504
%D 1984
%P 367-378
%L Rutovitz 68
%A D. Rutovitz
%T Data Structures for Operations on Digital Images
%B Pictorial Pattern Recognition
%E G. C. Cheng et al.
%I Thompson Book Co.
%C Washington, D. C.
%D 1968
%P 105-133
%L Smith and Merali 85
%A J. W. T. Smith
%A Z. Merali
%T Optical Character Recognition: The Technology and its Application in Information Units and Libraries
%R Library and Information Research Report 33
%I The British Library
%D 1985
%L Suen 86
%A C. Y. Suen
%T Character Recognition by Computer and Applications
%B Handbook of Pattern Recognition and Image Processing
%D 1986
%P 569-586
%L Wang 85
%A P. S. P. Wang
%T A New Character Recognition Scheme with Lower Ambiguity and Higher Recognizability
%J Pattern Recognition Letters
%V 3
%D 1985
%P 431-436
%L White and Rohrer 83
%A J.M. White
%A G.D. Rohrer
%T Image Thresholding for Optical Character Recognition and Other Applications Requiring Character Image Extraction
%J IBM Journal of Research and Development
%V 27
%N 4
%D July 1983
%P 400-411
%L Winzer 75
%A Gerhard Winzer
%T Character Recognition With a Coherent Optical Multichannel Correlator
%J IEEE Transactions on Computers
%V C-24
%N 4
%D April 1975
%P 419-423
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From: nad at computer-lab.cambridge.ac.uk
Hi,
I've only got two references for you - but they have 42 and 69 references,
respectively (some of the refs will be the same, but you get at least 69
references!).
They are:
"An overview of character recognition methodologies"
J. Mantas
Pattern Recognition, Volume 19, Number 6, 1986
pages 425-430
"Methodologies in pattern recognition and image analysis - a brief survey"
J. Mantas
Pattern Recognition, Volume 20, Number 1, 1987
pages 1-6
Neil Dodgson
============
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From: YAEGER.L at AppleLink.Apple.COM
I presume you know of "The 1989 Neuro-Computing Bibliography" edited by Casimir
C. Klimasauskas, a Bradford Book, from MIT Press. It lists 11 references for
character recognition in its index.
- larryy at apple.com
----------------------------------------------------------
From: Tetsu Fujisaki <TETSU at IBM.COM>
1. Suen, C. Y., Berthod, M., and Mori, S.,
"Automatic Recognition of Handprinted Characters - The State
of the Art", Proc. IEEE, 68, 4 (April 1980) 469-487
2. Tappert, C. C., Suen, C. Y., and Wakahara T,
"The State-of-the-Art in on-line handwriting recognition",
IEEE Proc. 9th Int'l Conf. on Pattern Recognition, Rome Italy,
Nov. 1988. Also in IBM RC 14045.
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From: burrow at grad1.cis.upenn.edu (Tom Burrow)
Apparently, the state of the art in connectionism, as a lot of people
will tell you, I'm sure, is Y. Le Cun et al's work which can be found
in NIPS 90. Other significant connectionist approaches are
Fukushima's neocognitron and Denker et al's work which I *believe* is
in NIPS 88.
I am interested in handprinted character recognition. Type set
character recognition is basically solved, and I believe you shouldn't
have any trouble locating texts on this (although I've only looked at
the text edited by Kovalevsky (sp?), which I believe is just entitled
"Reading Machines". Bayesian classifiers, which you can read about in
any statistical pattern recognition text (eg, Duda and Hart, Gonzalez,
etc), are capable of performing recognition, since one can choose
reliable features present in machine printed text (eg, moments,
projections, etc), and the segmentation problem is fairly trivial).
Perhaps the greatest problem in handprinted recognition is the
segmentation problem. Unfortunately, most connectionist approaches
fail miserably in this respect, relying on traditional methods for
segmentation which become a bottleneck. I am inspecting connectionist
methods which perform segmentation and recognition concurrently, and I
recommend you do not inspect the problems independently.
I am by no means expert in any area which I've commented on, but I
hope this helps. Also, again, please send me your compiled responses.
Thank you and good luck.
Tom Burrow
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