Recognition algorithms are difficult to write and difficult to maintain. There is need for better tools to support the creation, debugging, optimization, and comparison of recognition algorithms. We propose an approach that centers on a process-oriented description. The approach is implemented using a new scripting language called RSL (Recognition Strategy Language), which captures the recognition decisions an algorithm makes as it executes. This semi-formal process-oriented description provides a powerful basis for developing and comparing recognition algorithms. Based on this description, we describe new metrics related to the sequence of decisions an algorithm makes during recognition. The capture of intermediate decision outputs and these new process-oriented metrics greatly extend the limited information available from final results and traditional results-oriented metrics such as recall and precision. Using a simple example, we illustrate how these new metrics can be used to understand and improve decisions within a recognition strategy. We believe these new metrics may also be applied in machine learning algorithms that construct optimal decision sequences from sets of decisions and/or strategies.
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
Handley, J.: Table analysis for multi-line cell identification. In: Proc. Document Recognition and Retrieval VIII (IS&T/SPIE Electronic Imaging). Volume 4307., San Jose, CA (2001) 34-43
Phillips, I., Chen, S., Haralick, R.: CD-ROM document database standard. In: Proc. Second Int’l Conf. Document Analysis and Recognition, Tsukuba Science City, Japan (1993) 478-483
Silva, A.E., Jorge, A., Torgo, L.: Design of an end-to-end method to extract in-formation from tables. International Journal on Document Analysis and Recog-nition 8 (2006) 144-171
Embley, D., Hurst, M., Lopresti, D., Nagy, G.: Table-processing paradigms: a research survey. International Journal on Document Analysis and Recognition 8 (2006) 66-86
Handley, J.: Document recognition. In: Electronic Imaging Technology. IS&T/SPIE Optical Engineering Press, Bellingham, WA (1999)
Lopresti, D., Nagy, G.: Automated table processing: An (opinionated) survey. In: Proc. Third Int’l Workshop on Graphics Recognition, Jaipur, India (1999) 109-134
Lopresti, D., Nagy, G.: A tabular survey of automated table processing. In: Lecture Notes in Computer Science. Volume 1941. Springer-Verlag, Berlin (2000) 93-120
Zanibbi, R., Blostein, D., Cordy, J.: A survey of table recognition: Models, observations, transformations, and inferences. Int’l J. Document Analysis and Recognition 7(1) (2004) 1-16
Hurst, M.: Towards a theory of tables. International Journal on Document Anal- ysis and Recognition 8 (2006) 123-131
Zanibbi, R., Blostein, D., Cordy, J.R.: Recognition tasks are imitation games. In: Lecture Notes in Computer Science. Volume 3686. (2005) 209-218
Zanibbi, R., Blostein, D., Cordy, J.R.: The recognition strategy language. In: Proc. Eighth Int’l Conf. Document Analysis and Recognition. (2005) 565-569 Vol. 2
Hu, J., Kashi, R., Lopresti, D., Wilfong, G.: Table structure recognition and its evaluation. In: Proc. Document Recognition and Retrieval VIII (IS&T/SPIE Electronic Imaging). Volume 4307., San Jose, CA (2001) 44-55
Bottoni, P., Mussio, P., Protti, M.: Metareasoning in the determination of image interpretation strategies. Pattern Recognition Letters 15 (1994) 177-190
Draper, B.: Learning control strategies for object recognition. In Ikeuchi, K., Veloso, M., eds.: Symbolic Visual Learning. Oxford Press, New York (1997) 49-76
Draper, B., Bins, J., Baek, K.: Adore: Adaptive object recognition. Videre 1(4) (2000) 86-99 (online journal)
Haralick, R.: Document image understanding: Geometric and logical layout. In: Proc. Conf. Computer Vision and Pattern Recognition, Seattle, WA (1994) 385-390
Nagy, G.: Twenty years of document image analysis in PAMI. IEEE Trans. Pattern Analysis and Machine Intelligence 22(1) (2000) 38-62
Hurst, M.: Layout and language: Challenges for table understanding on the web. In: Proc. First Int’l Workshop on Web Document Analysis, Seattle, WA (2001) 27-30
Wang, Y., Hu, J.: Detecting tables in HTML documents. In: Lecture Notes in Computer Science. Volume 2423., Berlin, Springer-Verlag (2002) 249-260
Daumé, H., Langford, J., Marcu, D.: Search-based structured prediction. (un-published) (2006)
Daumeé, H., Marcu, D.: Learning as search optimization: Approximate large margin methods for structured prediction. In: Proc. International Conference on Machine Learning, Bonn (Germany) (2005) 169-176
Amano, A., Asada, N., Mukunoki, M., Aoyama, M.: Table form document anal-ysis based on the document structure grammar. International Journal on Doc-ument Analysis and Recognition 8 (2006) 201-213
Coüasnon, B.: DMOS, a generic document recognition method: Application to table structure analysis in a general and in a specific way. International Journal on Document Analysis and Recognition 8 (2006) 111-122
Takasu, A., Satoh, S., Katsura, E.: A document understanding method for database construction of an electronic library. In: Proc. Twelfth Int’l Conf. Pat-tern Recognition, Jerusalem, Israel (1994) 463-466
Bagdanov, A.: Style Characterization of Machine Printed Texts. PhD thesis, University of Amsterdam (2004)
Wang, X.: Tabular Abstraction, Editing and Formatting. PhD thesis, University of Waterloo, Canada (1996)
Grossman, J., ed.: 12 (Tables). In: Chicago Manual of Style. 14th edn. University of Chicago Press (1993)
Hurst, M.: Layout and language: An efficient algorithm for detecting text blocks based on spatial and linguistic evidence. In: Proc. Document Recognition and Retrieval VIII (IS&T/SPIE Electronic Imaging). Volume 4307., San Jose, CA (2001) 56-67
Ousterhout, J.: Tcl and the Tk Toolkit. Addison-Wesley (1994)
.van Melle, W., Shortliffe, E., Buchanan, B. In: EMYCIN, A Knowledge En- gineer’s Tool for Constructing Rule-Based Expert Systems. Addison-Wesley (1984) 302-328
Cordy, J.: The TXL source transformation language. Science of Computer Pro- gramming 61(3) (2006) 190-210
Cordy, J., Dean, T.R. Malton, A., Schneider, K.: Source transformation in soft-ware engineering using the TXL transformation system. Journal of Information and Software Technology 44(13) (2002) 827-837
Zanibbi, R., Blostein, D., Cordy, J.R.: Recognizing mathematical expressions using tree transformation. IEEE Trans. Pattern Analysis and Machine Intelli-gence 24 (2002) 1455-1467
Zanibbi, R.: A Language for Specifying and Comparing Table Recognition Strategies. PhD thesis, Queen’s University, Kingston (Canada) (2004)
Zanibbi, R., Blostein, D., Cordy, J.R.: Historical recall and precision: summa-rizing generated hypotheses. In: Proc. Eighth Int’l Conference on Document Analysis and Recognition. (2005) 202-206 Vol. 1
Horwitz, S., Reps, T.: The use of program dependence graphs in software engi-neering. In: Proc. 14th International Conference on Software Engineering, New York, NY, USA, ACM Press (1992) 392-411
Weiser, M.: Program slicing. In: Proc. Fifth Int’l Conference on Software Engi-neering, Piscataway, NJ, USA, IEEE Press (1981) 439-449
.Quillian, M.: Semantic memory. In Minsky, M., ed.: Semantic Information Pro- cessing. MIT Press (1968) 216-270
Hu, J., Kashi, R., Lopresti, D., Nagy, G., Wilfong, G.: Why table ground-truthing is hard. In: Proc. Sixth Int’l Conf. Document Analysis and Recognition, Seattle, WA (2001) 129-133
Lopresti, D.: Exploiting WWW resources in experimental document analysis research. In: Lecture Notes in Computer Science. Volume 2423., Berlin, Springer-Verlag (2002) 532-543
Cesarini, F., Marinai, S., Sarti, L., Soga, G.: Trainable table location in doc-ument images. In: Proc. Sixteenth Int’l Conf. Pattern Recognition. Volume 3., Québec City, Canada (2002) 236-240
Hu, J., Kashi, R., Lopresti, D., Wilfong, G.: Evaluating the performance of table processing algorithms. Int’l J. Document Analysis and Recognition 4(3) (2002) 140-153
Kieninger, T., Dengel, A.: Applying the T-RECS table recognition system to the business letter domain. In: Proc. Sixth Int’l Conf. Document Analysis and Recognition, Seattle, WA (2001) 518-522
Liang, J.: Document Structure Analysis and Performance Evaluation. PhD the- sis, University of Washington (1999)
Lopresti, D., Wilfong, G.: Evaluating document analysis results via graph prob-ing. In: Proc. Sixth Int’l Conf. Document Analysis and Recognition, Seattle, WA (2001) 116-120
Colman, A.: Game Theory & its Applications in the Social and Biological Sci-ences. Butterworth-Heinemann Ltd., London (1995)
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
Zanibbi, R., Blostein, D., Cordy, J.R. (2008). Decision-Based Specification and Comparison of Table Recognition Algorithms. 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_4
Download citation
DOI: https://doi.org/10.1007/978-3-540-76280-5_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76279-9
Online ISBN: 978-3-540-76280-5
eBook Packages: EngineeringEngineering (R0)