Metrics for evaluating performance in document analysis: Application to tables

International Journal on Document Analysis and Recognition

Volumn 14, Issue 1, 2011, Pages 101-109

  Silva, Ana Costa e ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

Document analysis; Metrics; Performance evaluation; Table processing

Indexed keywords

DOCUMENT ANALYSIS; EVALUATION METRICS; HIGH PRECISION; METRICS; PERFORMANCE EVALUATION; PRECISION AND RECALL; ROADMAP; TABLE LOCATION; TABLE PROCESSING;

ALGORITHMS;

HIDDEN MARKOV MODELS;

EID: 79952100756     PISSN: 14332833     EISSN: 14332825     Source Type: Journal    
DOI: 10.1007/s10032-010-0144-2     Document Type: Article

References (26)

1. Bishop C. M., Svensen M., Hinton G. E.: Distinguishing Text from raphics in On-line Handwritten Ink, pp. 142-147. IWFHR '04, USA (2004).
2. Cafarella, M. J., Halevy, A. Y., Zhang Y., Wang D. Z., Wu, E.: Uncovering the relational web. International Workshop on the Web and Databases, Canada (2008).
3. Cesarini F., Marinai S., Sarti L., Soda G.: Trainable Table Location in document Images, vol. 3, pp. 236-240. ICPR, Canada (2002).
4. Chao, H. (2003) Background pattern recognition in multi-page PDF documents. DLIA Workshop, UK, pp. 41-46.
5. Chen, H. H., Tsai, S. C., Tsai, J. H. (2000) Mining tables from large scale HTML texts. International Conference on Computational Linguistics, Germany, pp. 166-172.
6. Demšar J.: Statistical comparisons of classifiers over multiple datasets. J. Mach. Learn. Res. 7, 1-30 (2006).
7. Ghoson, A. M. A: Decision tree induction & clustering techniques in SAS Enterprise Miner, SPSS Clementine, and IBM Intelligent Miner-a comparative analysis. IABR & ITLC Conference Proceedings, Orlando, FL, USA (2010).
8. Hu J., Kashi R., Lopresti D., Wilfong G.: Medium Independent Table Detection. Document Recognition and Retrieval VII, vol. 3967, pp. 291-302. SPIE, USA (2000).
9. Hu J., Kashi R., Lopresti D., Nagy G., Wilfong G.: Evaluating the performance of table processing algorithms. Int. J. Document Anal. Recogn. 4(3), 140-153 (2002).
10. Hurst, M.: The interpretation of tables in text. PhD, Edinburgh University, UK (2000).
11. Hurst M.: A Constraint-Based Approach to able Structure Derivation, pp. 911-915. ICDAR, UK (2003).
12. Kboubi F., Chabi A. H., Ahmed M. B.: Table Recognition and Combination Methods, pp. 1237-1241. ICDAR, Korea (2005).
13. Jorge A. M., Lopes A. A.: Chapter 11: "iterative part-of-speech tagging", learning language in logic. In: Cussens, J., Dzeroski, S. (eds) Lecture Notes in Computer Science, vol. 1925, pp. 170-183. Springer, Berlin (2000).
14. Long V.: An RDF-based black-board architecture for improving table analysis, pp. 21-24. DAS, New Zealand (2006).
15. Ng, H. T., Lim, C. Y., Koo, J. L. T. (1999) Learning to recognize tables in free text. Annual Meeting of the Association for Computational Linguistics, USA, pp. 443-450.
16. Pinto, D., McCallum, A., Wei, X., Croft, W. B. (2003) Table extraction using conditional random fields. SIGIR, ACM, pp. 235-242.
17. Philips I., Chhabra A.: Empirical performance evaluation of graphics recognition systems. IEEE Trans. Pattern. Anal. Mach. Intell. 21(9), 849-870 (1999).
18. Pyreddi P., Croft W. B.: A system for retrieval in text tables. Technical Report 105, University of Massachusetts, Massachusetts, USA (1997).
19. Silva A. C., Jorge A. M., Torgo L. (2003) Selection of table areas for information extraction, in Proceedings of DLIA, UK, pp. 15-18.
20. Silva A. C., Jorge A. M., Torgo L.: Design of an end-to-end method to extract information from tables. Int. J. Document Anal. Recogn. 8(2-3), 144-171 (2006).
21. Silva A. C.: Learning Rich Hidden Markov Models in ocument Analysis: Table Location, pp. 843-847. ICDAR, Spain (2009).
22. Silva, A. C.: Parts that add up to a whole: a framework for the analysis of tables. PHD Thesis, Edinburgh University, UK (2010).
23. Tupaj S., Shi Z., Chang C. H., Alam H.: Extracting Tabular Information from Text Files. EECS Department, Tufts University, Medford, USA (1996).
24. Varian Hal R.: Intermediate Microeconomics: A Modern Approach, 5th edn. University of California, Berkeley, USA (1999).
25. Wang, Y.: Document analysis: a table structure understanding and zone content classification. PHD Thesis, University of Washington, USA (2002).
26. Wang, Y., Hu, J.: A machine learning based approach for table detection on the web. International World Wide Web Conference, USA, pp. 242-250 (2002).