Abstract
The handwritten/printed text discrimination problem is a decision problem usually solved after a binarization of grey level or color images. The decision is usually made at the connected component level of a filtered image. These image components are labeled as printed or handwritten. Each component is represented as a point in a n dimensional space based on the use of n different features. In this paper we present the transformation of a (state of the art) traditional system dealing with the handwritten/printed text discrimination problem to an agent-based system. In this system we associate two different agents with the two different points of view (i.e. linearity and regularity) considered in the baseline system for discriminating a text, based on four (two for each agent) different features. We are also using argumentation for modeling the decision making mechanisms of the agents. We then present experimental results that compare the two systems by using images of the IAM handwriting database. These results empirically prove the significant improvement we can have by using the agent-based system.
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References
Belaïd, A., Santosh, K.C., Poulain D’Andecy, V.: Handwritten and printed text separation in real document. In: Proceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013, Kyoto, Japan, pp. 218–221, 20–23 May 2013
Bench-Capon, T.J.M., Dunne, P.E.: Argumentation in artificial intelligence. Artif. Intell. 171(10–15), 619–641 (2007)
Bovenkamp, E.G.P., Dijkstra, J., Bosch, J.G., Reiber, J.H.C.: Multi-agent segmentation of IVUS images. Pattern Recogn. 37(4), 647–663 (2004)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, New York (2000)
Dung, P.M.: On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and \(n\)-person games. Artif. Intell. 77, 321–357 (1995)
Hamrouni, S., Cloppet, F., Vincent, N.: Handwritten and printed text separation: linearity and regularity assessment. In: Campilho, A., Kamel, M. (eds.) ICIAR 2014. LNCS, vol. 8814, pp. 387–394. Springer, Cham (2014). doi:10.1007/978-3-319-11758-4_42
Kakas, A., Moraitis, P.: Argumentation based decision making for autonomous agents. In: Proceedings of the 2nd International Joint Conference on Autonomous Agents and Multi-Agents Systems, pp. 883–890 (2003)
Kumar, J., Prasad, R., Cao, H., Abd-Almageed, W., Doermann, D., Natarajan, P.: Shape codebook based handwritten and machine printed text zone extraction. In: Document Recognition and Retrieval, San Francisco, vol. 7874, pp. 1–8, January 2011
Lins, R.D.: Meeting new challenges in document engineering. J. UCS 17(1), 1–2 (2011)
Peng, X., Setlur, S., Govindaraju, V., Sitaram, R.: Handwritten text separation from annotated machine printed documents using Markov random fields. IJDAR 16(1), 1–16 (2013)
Ricquebourg, Y., Raymond, C., Poirriez, B., Lemaitre, A., Coüasnon, B.: Boosting bonsai trees for handwritten/printed text discrimination. In: Document Recognition and Retrieval XXI, San Francisco, California, USA, pp. 902105–902105-12, 5–6 February 2014
Wall, K., Danielsson, P.E.: A fast sequential method for polygonal approximation of digitized curves. Comput. Vis. Graph. Image Process. 28(3), 220–227 (1984)
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Cloppet, F., Moraitis, P., Vincent, N. (2017). An Agent-Based System for Printed/Handwritten Text Discrimination. In: An, B., Bazzan, A., Leite, J., Villata, S., van der Torre, L. (eds) PRIMA 2017: Principles and Practice of Multi-Agent Systems. PRIMA 2017. Lecture Notes in Computer Science(), vol 10621. Springer, Cham. https://doi.org/10.1007/978-3-319-69131-2_11
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DOI: https://doi.org/10.1007/978-3-319-69131-2_11
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