Abstract
Automatically processing production documents requires document type detection as well as data capture to find appropriate index data from a post-OCR representation of the document. While current learning-based methods perform quite well due to many similar documents created with the same template, their machine learning models require intense training and are hard to update frequently. We provide a method for continuously incorporating user feedback in a layout-based extraction process taking care of both immediate learning as well as limiting the size of the model. The method is evaluated on a tagged corpus of more than 5,000 business documents. It allows not only continuous re-training of the model thus adapting it to new document templates, but also starting from scratch with an empty model requiring less than 10% of the corpus as training documents to reach an accuracy measure of more than 80%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, COLT 1998, Madisson, WI, USA, pp. 92–100 (1998)
Cauwenberghs, G., Poggio, T.: Incremental and decremental support vector machine learning. In: Advances in Neural Information Processing Systems, vol. 13, pp. 409–415. MIT Press (2001)
Culotta, A., Kristjansson, T., McCallum, A., Viola, P.: Corrective feedback and persistent learning for information extraction. Artif. Intell. 170, 1101–1122 (2006)
Esser, D., Schuster, D., Muthmann, K., Berger, M., Schill, A.: Automatic Indexing of Scanned Documents - a Layout-based Approach. In: Document Recognition and Retrieval XIX (DRR), San Francisco, CA, USA (2012)
Huang, Y., Mitchell, T.M.: Text clustering with extended user feedback. In: Proc. of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2006, Seattle, WA, USA, pp. 413–420 (2006)
Jia, Y., Yan, S., Zhang, C.: Semi-supervised classification on evolutionary data. In: Proceedings of the 21st International Jont Conference on Artifical intelligence, pp. 1083–1088. Morgan Kaufmann Publishers Inc., San Francisco (2009)
Raghavan, H., Madani, O., Jones, R.: Active learning with feedback on features and instances. J. Mach. Learn. Res. 7, 1655–1686 (2006)
Saund, E.: Scientific challenges underlying production document processing. In: Document Recognition and Retrieval XVIII, DRR 2011, San Francisco, CA, USA (2011)
Stumpf, S., Rajaram, V., Li, L., Burnett, M., Dietterich, T., Sullivan, E., Drummond, R., Herlocker, J.: Toward harnessing user feedback for machine learning. In: Proceedings of the 12th International Conference on Intelligent User Interfaces, IUI 2007, Honolulu, HI, USA, pp. 82–91 (2007)
Stumpf, S., Rajaram, V., Li, L., Wong, W.K., Burnett, M., Dietterich, T., Sullivan, E., Herlocker, J.: Interacting meaningfully with machine learning systems: Three experiments. Int. J. Hum.-Comput. Stud. 67, 639–662 (2009)
Wong, W.K., Oberst, I., Das, S., Moore, T., Stumpf, S., McIntosh, K., Burnett, M.: End-user feature labeling: a locally-weighted regression approach. In: Proceedings of the 16th International Conference on Intelligent User Interfaces, IUI 2011, Palo Alto, CA, USA, pp. 115–124 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hanke, M., Muthmann, K., Schuster, D., Schill, A., Aliyev, K., Berger, M. (2012). Continuous User Feedback Learning for Data Capture from Business Documents. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28931-6_51
Download citation
DOI: https://doi.org/10.1007/978-3-642-28931-6_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28930-9
Online ISBN: 978-3-642-28931-6
eBook Packages: Computer ScienceComputer Science (R0)