This chapter explores three aspects of learning in document analysis: (1) field classification, (2) interactive recognition, and (3) portable and networked applications. Context in document classification conventionally refers to language context, i.e., deterministic or statistical constraints on the sequence of letters in syllables or words, and on the sequence of words in phrases or sentences. We show how to exploit other types of statistical dependence, specifically the dependence between the shape features of several patterns due to the common source of the patterns within a field or a document. This type of dependence leads to field classification, where the features of some patterns may reveal useful information about the features of other patterns from the same source but not necessarily from the same class. We explore the relationship between field classification and the older concepts of unsupervised learning and adaptation. Human interaction is often more effective interspersed with algorithmic processes than only before or after the automated parts of the process. We develop a taxonomy for interaction during training and testing, and show how either human-initiated and machine-initiated interaction can lead to human and machine learning. In a section on new technologies, we discuss how new cameras and displays, web-wide access, interoperability, and essentially unlimited storage provide fertile new approaches to document analysis.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Grother, P.: Handprinted Forms and Character Database, NIST Special Database 19, technical report, March. 1995.
McCarthy, J.: Notes on formalizing contexts. In Kehler, T., and Rosenschein, S., eds., Proceedings of the Fifth National Conference on Artificial Intelligence, pp. 555-560. Los Altos, CA, Morgan Kaufmann, 1986.
Veeramachaneni, S., Sarkar, P., Nagy, G.: Modeling Context as Statistical De-pendence, in Procs. Modeling and Using Context: 5th International and In-terdisciplinary Conference CONTEXT 2005, Paris, France, July 5-8, (2005). Lecture Notes in Computer Science, Volume 3554, pp. 515-528, Jul 2005.
Bouquet, P., Serafini L.: Comparing formal theories of context in AI. Artificial Intelligence, (2004). 155: pp. 1-67.
Modeling and Using Context: Second International and Interdisciplinary Con- ference, CONTEXT’99, Trento, Italy, September pp. 9-11, (1999), Proceedings Lecture Notes in Computer Science Vol. 1688 Bouquet, P.; Serafini, L.; Brezillon, P.; Benerecetti, M.; Castellani, F. (Eds.)
Modeling and Using Context: 4th International and Interdisciplinary Confer-ence, CONTEXT 2003, Stanford, CA, USA, June 23-25, 2003, Proceedings Series: Lecture Notes in Computer Science, Vol. 2680 Blackburn, P.; Ghidini, C.; Turner, R.M.; Giunchiglia, F. (Eds.) (2003).
Modeling and Using Context: 5th International and Interdisciplinary Confer-ence, CONTEXT 2005, Paris, France, July 5-8, 2005, Proceedings Series: Lec-ture Notes in Computer Science, Vol. 3554 Dey, A.; Kokinov, B.; Leake, D.; Turner, R. (Eds.) (2005).
Yannakoudakis, E., Angelidakis, G.: An insight into the entropy and redun- dancy of the English dictionary, IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(6), 960-970, 1988.
Suen, C.Y.: N-gram statistics for natural language understanding and text processing, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2),164-172, 1979
Katz, S. M. : Estimation of probabilities from sparse data for the language model component of a speech recognizer, IEEE Transactions on Acoustics, Speech and Signal Processing, 35(3):400-401, March 1987.
Rice, S., Nagy, G., Nartker T.: Optical Character Recognition: An Illustrated Guide to the Frontier, Kluwer Academic Publishers, Boston/Dordrecht/London, 1999.
Hull, J.J., Srihari S.N.: Experiments in Text Recognition with Binary N-Gram and Viterbi Algorithms, IEEE Trans. Pattern Analysis and Machine Intelligence, 4(5), 520-530, Sept. 1982.
Hull, J.J.: A hidden Markov model for language syntax in text recognition. In Proceedings of the Eleventh Conference on Pattern Recognition, volume 2, 124-127, 1992.
Hull, J.J.: Incorporating language syntax in visual text recognition with a statistical model. IEEE Trans. Pattern Analysis and Machine Intelligence, 18(12):1251-1256, 1996.
Nagy, G.: Teaching a Computer to Read, Proc. 11th Int’l Conf. Pattern Recog- nition, vol. 2, pp. 225-229, 1992.
Raviv, J.: Decision Making in Markov Chains Applied to the Problem of Pat- tern Recognition, IEEE Trans. Information Theory, VOL. IT-13, no. 4, 536-551,1967.
Toussaint, G. T.: The use of context in pattern recognition, Pattern Recognition, Vol. 10, 189-204, 1978.
Shinghal, R., Toussaint, G.T., Experiments in text recognition with the modi- fied Viterbi algorithm, IEEE Trans. Pattern Analysis and Machine Intelligence 1(2),184-193, 1979.
Shinghal, R., Toussaint, G.T.: The sensitivity of the modified Viterbi algorithm to the source statistics, IEEE Trans. Pattern Analysis and Machine Intelligence 2(2),1181-1184, 1980.
Sinha, R.M.K., Prasada, B.: Visual Text Recognition through Contextual Pro-cessing, Pattern Recognition, 20(5), 463-479, 1988.
Sinha, R.M.K., Prasada, B., Houle, G. F., Sabourin, M.: Hybrid Contextural Text Recognition with String Matching, IEEE Trans. Pattern Anal. Mach. Intell. 15(9), 915-925 (1993).
Rabiner, L.R. : A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proceedings of the IEEE, 77(2), 257-286, 1989.
Gilloux, M., Leroux, M. Bertille J.M.: Strategies for Handwritten Words Recog-nition Using Hidden Markov Models, Proc. Second Int’l Conf. Document Anal-ysis and Recognition, 299-304, 1993.
Kuo, S.S., Agazzi, O.E.: Visual keyword recognition using hidden Markov mod-els, Proc. of IEEE Computer Society Conference on Computer Vision and Pat-tern Recognition, 329-334, 1993.
Nathan, K.A., Bellegarda, J.R., Nahamoo, D., Bellegarda, E.J.: On-Line Hand-writing Recognition Using Continuous Parameter Hidden Markov Models, Proc. Int’l Conf. Acoustics, Speech, and Signal Processing, vol. 5, 121-124, 1993.
MacKay, D.J.C., Peto, L.: A hierarchical Dirichlet language model. Natural Language Engineering, 1(3):1-19, 1994.
Bazzi, I., Schwartz, R, Makhoul, J.: An Omnifont Open-Vocabulary OCR Sys-tem for English and Arabic, IEEE Trans. Pattern Analysis and Machine In-telligence, vol. 21(6) 495-504, June 1999.
Feng, S., Manmatha, R., McCallum, A.: Exploring the Use of Conditional Ran-dom Field Models and HMMs for Historical Handwritten Document Recogni-tion, Proc. 2nd IEEE International Conference on Document Image Analysis for Libraries, DIAL 2006, Lyon, France, April 2006.
Sarkar, P., Nagy, G.: Classification of Style-Constrained Pattern-Fields, Proc. 15th Int’l Conf. Pattern Recognition, 859-862, 2000.
Sarkar, P., Nagy, G.: Style Consistency in Isogenous Patterns, Proc. Sixth Int’l Conf. Document Analysis and Recognition, pp. 1169-1174, 2001.
Sarkar, P., Nagy, G.: Style Consistent Classification of Isogenous Patterns, IEEE Trans. Pattern Analysis and Machine Intelligence, 27(1), Jan. 2005.
Veeramachaneni, S., Nagy, G.: Analytical Results on Style-constrained Bayesian Classification of Pattern Fields, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29(7) 1280-1285, July 2007.
Spitz, A.L.: An OCR based on character shape codes and lexical information, Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) Volume 2, Page: 723, 1995.
Spitz, A. L., Maghbouleh, A.: Text Categorization using Character Shape Codes, SPIE Symp on Electronic Image Science and Technology, San Jose, pp. 174-181, 2000.
Ho, T.K., J.J. Hull, S.N. Srihari: A Computational Model for Recognition of Multifont Word Images, Machine Vision and Applications 5, 157-168, 1992.
Ho, T.K., J.J. Hull, S N. Srihari: A Word Shape Analysis Approach to Lexicon Based Word Recognition, Pattern Recognition Letters 13, 821-826, 1992.
Hong, T., Hull, J.J.: Visual Inter-Word Relations and Their Use in OCR Post-processing, Proc. Third Int’l Conf. Document Analysis and Recognition, vol. 1, pp. 442-445, 1995.
Cesarini, F., Gori, M., Marinai, S., Soda, G.: INFORMys: A flexible invoice-like for reader system, IEEE Trans. on Pattern Recognition and Machine Intelli-gence, 20(7), 730-745, July 1998
Marinai, S., Marino, E., Soda, G.: Font Adaptive Word Indexing of Modern Printed Documents, IEEE Trans. Pattern Recognition and Machine Intelli-gence 28(8), 1187-1199, August 2006.
Shi, H., Pavlidis, T.: Font Recognition and Contextual Processing for More Accurate Text Recognition, Proc. Fourth Int’l Conf. Document Analysis and Recognition, vol. 1, pp. 39-44, 1997.
Zramdini, A.W., Ingold, R.: Optical Font Recognition from Projection Profiles, Electronic Publishing 6(3): 249-260 (1993).
Zramdini, A.W., Ingold, R.: Optical Font Recognition Using Typographical Features, IEEE Trans. Pattern Analysis and Machine Intelligence, 20(8), 877-882, Aug. 1998.
Bapst, F., Ingold, R.: Using Typography in Document Image Analysis. In Proc. Raster Imaging and Digital Typography (RIDT’98), Saint-Malo (France), pp. 240-251, 1998.
Srihari, S.N., Bandi, K., Beal, M.: A Statistical Model for Writer Verification, Proc. Int. Conf. on Document Analysis and Recognition (ICDAR-05) Seoul, Korea, August 2005.
Kawatani, T.: Character Recognition Performance Improvement Using Per-sonal Handwriting Characteristics, Proc. Third Int’l Conf. Document Analysis and Recognition, vol. 1, pp. 98-103, 1995.
Veeramachaneni, S., Nagy, G.: Style Context with Second-Order Statistics, IEEE Trans. Pattern Analysis and Machine Intelligence, 27(1), Jan. 2005.
Andra, S.: Non-parametric approaches to style-consistent classification, Rens-selaer Polytechnic Institute PhD dissertation, December 2006.
Andra, S., Nagy, G.: Combining Dichotomizers for MAP Field Classification, Proceedings of International Conference on Pattern Recognition-XVIII, Hong Kong, September 2006.
Widrow, B, Hoff, M.E.: Adaptive switching circuits, 1960 IRE WESCON Conv. Record, Part 4, 96-104, 1960.
Aizerman, M.A., Braverman, E.M., Rozonoer, L.I.: The Robbins-Monroe pro-cess and the method of potential functions, Automation and Remote Control 26,1882-1885, November 1965.
Tsypkin, Y. Z.: Adaptation, training, and self-organization in automatic sys-tems, Automation and Remote Control, vol. 27, pp. 1652, January 1966.
Fu, K.S.: Learning techniques in pattern recognition systems, in Pattern Recog-nition (L.N. Kanal, ed.) Thompson Book Company, Washington, 1968.
Jain, A., Duin, R., Mao, J.: Statistical Pattern Recognition A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1):4-37, 2000.
Zadeh, L.A.: On the definition of adaptivity, Proceedings of the IRE 51, #3. 469-470, 1963.
Lendaris, G.G.: On the Definition of Self-Organizing Systems, Proceedings of the IEEE 52, 3, March, 1964
Nagy, G.: Pattern Recognition IEEE 1966 Workshop, IEEE Spectrum, pp. 92-94, February 1967.
Castelli, V., Cover, T.: On the exponential value of labeled samples, Pattern Recognition Letters 16, 105-111, 1995.
Castelli, V., Cover, T.: The relative value of labeled and unlabeled samples in pattern recognition with an unknown mixing parameter, IEEE-Trans. Infor-mation Theory 42(6), 2101-2117, 1996.
Scudder, H.J.: Probability of error of some adaptive pattern-recognition ma-chines, IEEE. Trans. Information Theory IT-11, 363-371, July 1965.
Spragins, J.: Learning without a teacher, IEEE Trans. Information Theory, vol. IT-12, 223-229, April 1966.
Stanat, D.F.: Unsupervised learning of mixtures of probability functions, in Pattern Recognition (L.N. Kanal, ed.) Thompson Book Company, Washington, 1968.
Aizerman, M.A., Braverman, E.M., Rozonoer, L.I.: The probability problem of pattern recognition learning and the method of potential functions, Automation and Remote Control 25, 1175-1192, September 1964.
Braverman, E.M.: Experiments on machine learning to recognize visual pat-terns, translated from Automat. i Telemekh., vol. 23, pp. 349-364, March 1962, Automation and Remote Control, vol. 23, 315-327, 1962.
Braverman, E.M.: The method of potential functions in the problem of training machines to recognize patterns without a trainer, Automation and Remote Control, vol. 27, 1748-1771, October 1966.
Dorofeyuk, A.A.: Teaching algorithm for a pattern recognition machine without a teacher, based on the method of potential functions, Automation and Remote Control, vol. 27, 1728-1737, October 1966.
Nagy, G.: State of the Art in Pattern Recognition, Proceedings of the IEEE 56, #5, 336-362, May 1968.
Jain, A.K.: Cluster Analysis, Chapter 2 in Handbook of Pattern Recognition and Image Processing (K-S Fu and T-Y Young, eds), Academic Press, NY 1986.
Ball, G.H.: Data analysis in the social sciences: What about the details? Procs. Fall Joint Computer Conference, pp. 533-560, Spartan Books, 1965.
MacQueen, J.: Some methods for classification and analysis of multivariate observations, Proc. 5th Berkeley Symp on Statistics and Probability, pp. 281-297, Berkeley, CA University of California Press, 1967.
Ball, G.H., Hall, D.J.: A clustering technique for summarizing multivariate data, Behavioral Science, 12, pp. 153-155, March 1967.
Linde, Y., Buzo, A., Gray, R.M. : An algorithm for vector quantization design, IEEE Trans. Comm. 28, 84-95, 1980
Gersho, A., Gray, R. M. : Vector Quantization and Signal Compression, The International Series in Engineering and Computer Science, 1991.91
Dubes, R., Jain, A.K.: Validity studies in clustering methodologies, Pattern Recognition 11, 235-254, 1979.
Jain, A.K., Dubes, R.: Algorithms for Clustering Data, Prentice Hall 1988.
Theodoridis, S., Koutroumbas, T.: Pattern Recognition, Academic Press, 1999.
Topchy, A., Jain, A.K., Punch, W.: Clustering Ensembles: Models of Consensus and Weak Partitions, IEEE Trans. Pattern Analysis and Machine Intelligence, 27(12),1866-1881, Dec 2005.
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. New York: John Wiley and Sons, 2001.
Nagy, G., Shelton, G.L. : Self-Corrective Character Recognition System, IEEE Transactions on Information Theory IT-12, #2, pp. 215-222, April 1966.
Baird, H.S., Nagy, G.: A Self-Correcting 100-Font Classifier, Document Recog-nition, Proc., IS&T/SPIE Symp. on Electronic Imaging: Science Technology, San Jose, CA, February 6-10, 1994, L. Vincent and T. Pavlidis, eds., vol. 2181, pp. 106-115, 1994.
Breuel, T., Mathis, C.: Classification Using a Hierarchical Bayesian Approach, Proc. 16th Int’l Conf. Pattern Recognition, 40103-40106, Aug. 2002.
Sarkar, P., Baird, H.S., Zhang, X.: Training on Severely Degraded Text- Line Images, Proc. Seventh Int’l Conf. Document Analysis and Recognition, pp. 38-43, Aug. 2003.
Veeramachaneni, S., Nagy, G.: Adaptive Classifiers for Multisource OCR, Int’l J. Document Analysis and Recognition, 6(3), 154-166, Aug. (2004).
Marosi, I., Tóth, L.: OCR Voting Methods for Recognizing Low Contrast Printed Documents: in Proc. 2nd IEEE International Conference on Document Image Analysis for Libraries, DIAL 2006, Lyon, France, April 2006.
Gold, B.: Machine recognition of hand-sent Morse code, IRE Trans. Informa-tion Theory, vol. IT-5, pp. 17-24, March 1959.
Lucky, R. W.: Automatic Equalization for Digital Communication. Bell Sys- tems Technical Journal, 44:547-588, 1965.
Lucky, R. W.: Techniques for adaptive equalization of digital communication systems. Bell Systems Technical Journal, 45:255-286, February 1966.
Nagy, G., Tolaba, J.: Nonsupervised Crop Classification through Airborne Mul-tispectral Observations, IBM Journal of Research and Development 16, #2, pp. 138-153, March 1972.
Shahshani, B.M., Landgrebe, D.A.: Asymptotic improvement of supervised learning by utilizing additional unlabeled samples: normal mixture density case, Proc. SPIE Vol. 1766, p. 143-155, Neural and Stochastic Methods in Image and Signal Processing, Su-Shing Chen; Ed., 1992.
Dempster, A.P., Laird, M.M., Rubin, D.B.: Maximum Likelihood from Incom-plete Data via the EM Algorithm, J Royal Statistical Soc., vol. 39, no. 1, pp. 1-38, 1977.
Redner, R.A., Walker, H.F.: Mixture densities, maximum likelihood, and the EM algorithm, SIAM Review 26, 2, pp. 195-235, 1984.
Raudys, S., Jain, A.K.: Small Sample Size Effects in Statistical Pattern Recog- nition: Recommendations for Practitioners, IEEE Trans. on Patt. Anal. and Machine Intell., 13(3), 252-264, 1991.
Casey, R.G., Nagy, G.: Recognition of Printed Chinese Characters, IEEE Transactions on Electronic Computers EC-15, #1, pp. 91-101, February 1966.
Liu, C.-L., Jaeger, S., Nakagawa, M.: On line recognition of Chinese characters: the State of the Art, IEEE Trans. Pattern Analysis and Machine Intelligence 26,2, pp. 198-213, (2004).
Casey, R.G., Nagy, G.: Autonomous Reading Machine, IEEE Transactions on Computers C-17, #5, pp. 492-503, May 1968.
Casey, R.G., Nagy, G.: Advances in Pattern Recognition, Scientific American 224, #4, pp. 56-71, 1971.
Casey, R.G.: Text OCR by Solving a Cryptogram, Proc. Eighth Int’l Conf. Pattern Recognition, pp. 349-351, 1986.
Nagy, G., Seth, S., Einspahr, K., Meyer, T.: Efficient Algorithms to Decode Substitution Ciphers with Applications to OCR,Proceedings of International Conference on Pattern Recognition, vol. 8, 352-355, Paris, October 1986.
Ho, T.K., Nagy, G.: OCR with no shape training,Proceedings of International Conference on Pattern Recognition-XV, vol. 4, pp. 27-30, Barcelona, Septem-ber 2000.
Ascher, R.N., Nagy, G.: A Means for Achieving a High Degree of Compaction on Scan-Digitized Printed Text, IEEE Transactions on Computers C-23, #11, pp. 1174-1179, October 1974.
Bottou, L. Haffner, P., Howard, P., Simard, P., Bengio, Y., LeCun, Y.: High Quality Document Image Compression with DjVu, Journal of Electronic Imaging, vol. 7, no. 3, pp. 410-425, July 1998.
Witten, I., Moffat, A., Bell, T.: Managing Gigabytes, Academic Press 1999.
Marinai, S., Marino, E., Soda, G.: Font Adaptive Word Indexing of Modern Printed Documents, IEEE Trans. Pattern Recognition and Machine Intelli-gence 28(8), pp. 1187-1199, August 2006.
Nagy, G.: Twenty Years of Document Image Analysis in IEEE PAMI, IEEE Trans. Pattern Analysis and Machine Recognition 22(1), 38-62, January 2000.
Baird, H.S., Lopresti, D. P., editors: Human Interactive Proofs, Procs. Second International Workshop, volume 3517 of LNCS, 2005.
Miller, G.: The magical number seven plus or minus two; some limits on our capacity for processing information. Psychological Review, 63:81-97, 1956.
Sammon, J.W.: Interactive pattern analysis and classification, IEEE Trans. Computers C-16, 594-616, July 1970.
Ho, T.K.: Exploratory Analysis of Point Proximity in Subspaces, Proceedings of the 16th International Conference on Pattern Recognition, Quebec City, August 11-15, 2002.
Cesarini, F., Marinai, S., Sarti, L., Soda, G.: Trainable table location in docu- ment images, Proceedings of International Conference on Pattern RecognitionXVI, Vol. 3, Quebec City, 236-240, 2002.
Edwards, J.. New interfaces: Making computers more accessible. IEEE Com- puter, pages 12-14, December 1997.
Ancona, M., Locati, S., Mancini, M., Romagnoli, A., Quercini, G.: Comfortable textual data entry for PocketPC: the WTX system. In Advances in Graphonomics, Proceedings of International Graphonomics Symposium 2005, Salerno, Italy, June 2005.
Langendorf, D.J.: Textware solution’s Fitaly keyboard v1.0 easing the burden of keyboard input. WinCELair Review, February 1998.
Masui, T.: An efficient text input method for pen-based computers. In Proceed- ings of the ACM Conference on Computer-Human Interaction, pages 328-335, 1998.
James, C.L., Reischel, K.M.: Text input for mobile devices: Comparing model prediction to actual performance. In Proceedings of the ACM Conference on Computer-Human Interaction, pages 365-371, 2001.
Mackenzie, S., Soukore, W.: Text entry for mobile computing: Models and methods, theory and practice, Human-Computer Interaction, 17:147-198, 2002.
Nagy, G., Li, L., Samal, A., Seth, S., Xu, Y.: Integrated text and line-art ex-traction from a topographic map.International Journal on Document Analysis and Recognition, 2(4):177-185, June 2000.
English, W.K., Engelbart, D.C., Berman, M.L.: Display-selection techniques for text manipulation. IEEE Transactions on Human Factors in Electronics, HFE-8(1):5-15, March 1967.
Zou, J. Nagy, G.: Interactive visual pattern recognition,Proceedings of the 17th International Conference on Pattern Recognition, XVI, IEEE Computer Society Press, Vol. III, pp. 478-481, Aug. 2002.
Zou, J. Nagy, G.: Evaluation of model-based interactive ower recognition. In Proceedings of the 17th International Conference on Pattern Recognition, volume 2, pages 311-314, (2004).
Cha, S.-H., Evans, A., Gattani, A., Nagy, G., Sikorski, J., Tappert, C., Thomas, P., Zou, J.: Computer Assisted Visual Interactive Recognition (CAVIAR) technology. In Proceedings of the IEEE Electro/Information Technology Conference, May 2005. PDF
Nagy, G.: Interactive, Mobile, Distributed Pattern Recognition, Proc. of the 13th International Conference on Image Analysis and Processing ICIAP, Cagliari, Italy, LNCS 3617, 37-49, 2005.
Zou, J. Nagy, G.: Human-computer interaction for complex pattern recogni- tion problems, to appear in Data Complexity in Pattern Recognition, Springer Verlag, Editors: Mitra Basu, Tin Kam Ho, Publication Date: Dec. 2006.
Holland, M., Schlesiger, C.: High-mobility machine translation for a battlefield environment. In Proceedings of NATO/RTO Systems Concepts and Integration Symposium, volume 15, pages 1-3, Monterey, CA, May 1998.
Swan, K.: FALCon: Evaluation of OCR and machine translation paradigms, August 1999. http://www.arl.army.mil/seap/reports/kreport.pdf. (accessed on 8/8/06)
Fisher, F.: Digital camera for document acquisition. In Symposium on Document Image Understanding Technology, Columbia, MD, 2001. http://www.dtic.mil/matris/sbir/sbir022/a038.pdf. (accessed on 8/8/06)
Jacobs, C., Simard, P.. Low resolution camera based OCR. International Jour- nal on Document Analysis and Recognition. To appear. (2004)
Fujisawa, H., Sako, H., Okada, Y., Lee, S.: Information capturing camera and developmental issues. InProceedings of the Fifth International Conference on Document Analysis and Recognition (ICDAR’99), pages 205-208, Bangalore, India, September 1999.
Yang, J., Chen, X., Zhang, J., Zhang, Y., Waibel, A.: Automatic detection and translation of text from natural scenes. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP ’02), May 2002.
Hedgpeth, T., Rush, M., Black, J., Panchanathan, S.: The iCare project reader. In Procs. Sixth International ACM SIGACCESS Conference on Computers and Accessibility, October (2004).
Peters, J.P., Thillou, C., Ferreira, S.: Embedded reading device for blind people: a user-centred design. In Procs. IEEE Emerging Technologies and Applications for Imagery Pattern Recognition (AIPR 2004), pages 217-222, (2004).
Srihari, S., Shi, Z.: Forensic handwritten document retrieval system. In Procs. First International Workshop on Document Image Analysis for Libraries (DIAL’04), pages 188-194, (2004).
Xu, Y., Nagy, G.: Prototype Extraction and Adaptive OCR, IEEE Trans. Pattern Analysis and Machine Intelligence Vol. 21, 12, pp. 1280-1296, Dec. 1999.
MacKay, D.: Information-based objective functions for active data selection. Neural Computation, Vol. 4, No. 4, pp. 590-604, 1992.
Freund, Y., Seung, H.S., Shamir, E., Tishby, N.: Selective sampling using the query by committee algorithm. Machine Learning 28, 133-168, 1997
Liang, J., Doerman, D., Li, H.: Camera-based analysis of text and documents: a survey, International Journal on Document Analysis and Recognition, 7(2-3), 84-104, July 2005.
Pollard, S., Pilu, M.: Building cameras for capturing documents, International Journal on Document Analysis and Recognition, 7,(2-3), 123-137, July 2005.
Lopresti, D., Nagy, G.: Mobile Interactive Support System for Time-Critical Document Exploitation, Procs. Symposium on Document Image Understand-ing, College Park, MD November 2005.
Gandhi, T., Kasturi, R., Antani, S.: Application of planar motion segmentation for scene text extraction. In Proc. of the ICPR, 2000, I: 445-449.
Myers, G., Bolles, R., Luong, Q.-T., Herson, J.: Recognition of text in 3-D scenes. In Proc. of the 4th Symp. on Document Image Understanding Technology, pp. 23-25, 2001.
Wu, W., Chen, X., Yang, J.: Incremental Detection of Text on Road Signs from Video with Application to a Driving Assistant System, Proceedings of ACM Multimedia 2004 (MM2004), pp. 852-859 (2004).
Yamaguchi, T., Maruyama, M., Miyao1, H., Nakano, Y.: Digit recognition in a natural scene with skew and slant normalization, International Journal on Document Analysis and Recognition, 7(2-3), 168-177, July 2005.
Salzberg, S.L.: On comparing classifiers: Pitfalls to avoid and a recommended approach, Data Mining and Knowledge Discovery 1, 317-327, 1997.
Lopresti, D., Zhou, J.: Document analysis and the World Wide Web, In Pro-ceedings of the Second IAPR Workshop on Document Analysis Systems, pages 651-659, Malvern, PA, Oct. 1996.
Crane, G.: What Do You Do with a Million Books? D-Lib Magazine 12(3), ISSN 1082-9873, March 2006.
Rice, S.V., Bailey, S.M.: A Web Search Engine for Sound Effects, in Proceedings of the 119th Convention of the Audio Engineering Society, Paper #6622, New York, (2005) (PDF).
Nagy G., Lopresti, D.: Interactive Document Processing and Digital Libraries, Proc. 2nd IEEE International Conference on Document Image Analysis for Libraries, Lyon, France, IEEE Press, 2006.
Chou, P.A.: Recognition of equations using a two-dimensional stochastic context-free grammar. In W. A. Pearlman, editor, Visual Communications and Image Processing IV, vol. 1199 of SPIE Proceedings Series, 852-863, 1989.
Twaakyondo, H.M., Okamoto, M.: Structure analysis and recognition of mathe-matical expressions, Proc. third Inter. Conf. on Document Analysis and Recog-nition, ICDAR’95, Montral, Canada, pp. 430-437, 1995.
Zanibbi, R., Blostein, D., Cordy, J.R.: Recognizing Mathematical Expressions Using Tree Transformation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (11), 1455-1467, 2002.
Wang, X.: Tabular abstraction, editing, and formatting, PhD dissertation, Uni- versity of Waterloo, Canada, 1006.
Embley, D., Lopresti, D., Nagy, G.: Notes on Contemporary Table Recogni- tion, Document Analysis Systems VII, 7th International Workshop, Procs. DAS 2006, Nelson, New Zealand, February 13-15, 2006, Horst Bunke, A. Lawrence Spitz (Eds.) LNCS 3872, pp. 164-175 Springer 2006.
Macgregor, G., McCulloch, E.: Collaborative tagging as a knowledge organi- sation and resource discovery tool, Library Review, Volume: 55 Issue: 5 Page: 291-300, 2006
Hitz, O., Robadey, L., Ingold, R.: Using XML in Document Recognition, In Proc. Document Layout Interpretation and its Applications (DLIA’99), Bangalore (India), 1999.
Tijerino, Y.A., Embley, D. W., Lonsdale, D. W., Nagy, G.: Towards Ontology generation from tables, World Wide Web Journal 8, 3, Springer, September 2005.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Nagy, G., Veeramachaneni, S. (2008). Adaptive and Interactive Approaches to Document Analysis. In: Marinai, S., Fujisawa, H. (eds) Machine Learning in Document Analysis and Recognition. Studies in Computational Intelligence, vol 90. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76280-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-76280-5_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76279-9
Online ISBN: 978-3-540-76280-5
eBook Packages: EngineeringEngineering (R0)