Abstract
Many methods of graphics recognition have been developed throughout the years for the recognition of pre-segmented graphics symbols but very few techniques achieved the objective of symbol spotting and recognition together in a generic case. To go one step forward through this objective, this paper presents an original solution for symbol spotting using a graph represen-tation of graphical documents. The proposed strategy has two main step. In the first step, a graph based representation of a document image is generated that includes selection of description primitives (nodes of the graph) and organisation of these features (edges). In the second step the graph is used to spot interesting parts of the image that potentially correspond to symbols. The sub-graphs associated to selected zones are then submitted to a graph matching algorithm in order to take the final decision and to recognize the class of the symbol. The experimental results obtained on different types of documents demonstrates that the system can handle different types of images without any modification.
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Joseph, S.H., Pridmore, T.P.: Knowledge Directed Interpretation of Mechanical Engineering Drawings. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(9), 928–940 (1992)
DenHartog, J.E., TenKate, T.K., Gerbrands, J.J.: Knowledge Based Interpretation of Utility Maps. Computer Vision and Image Understanding 63(1), 105–117 (1996)
Song, J., Su, F., Tai, M., Cai, S.: An Object-Oriented Progressive-Simplification-Based Vectorization System for Engineering Drawings: Model, Algorithm, and Performance. IEEE Transaction on Pattern Analysis and Machine Intelligence 24(8), 1048–1060 (2002)
Lladós, J., Dosch, P.: Vectorial Signatures for Symbol Discrimination. In: Lladós, J., Kwon, Y.-B. (eds.) GREC 2003. LNCS, vol. 3088, pp. 154–165. Springer, Heidelberg (2004)
Wang, Y., Phillips, I.T., Haralick, R.M.: Document Zone Content Classification and its Performance Evaluation. Pattern Recognition 39, 57–73 (2006)
Wenyin, L., Zhang, W., Yan, L.: An Interactive Example-Driven Approach to Graphics Recognition in Engineering Drawings. International Journal on Document Analysis and Recognition 9, 13–29 (2007)
Ramel, J.Y., Vincent, N., Emptoz, H.: A Structural Representation for Understanding Line Drawing Images. International Journal on Document Analysis and Recognition 3(2), 58–66 (2000)
Qureshi, R.J., Ramel, J.Y., Cardot, H.: Graphic Symbol Recognition Using Flexible Matching Of Attributed Relational Graphs. In: Proceeding of 6th IASTED International Conference on Visualization, Imaging, and Image Processing (VIIP), Palma de Mallorca-Spain, pp. 383–388 (2006)
Valveny, E., et al.: A general framework for the evaluation of symbol recognition methods. IJDAR 9, 59–74 (2007)
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Qureshi, R.J., Ramel, JY., Barret, D., Cardot, H. (2008). Spotting Symbols in Line Drawing Images Using Graph Representations. In: Liu, W., Lladós, J., Ogier, JM. (eds) Graphics Recognition. Recent Advances and New Opportunities. GREC 2007. Lecture Notes in Computer Science, vol 5046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88188-9_10
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DOI: https://doi.org/10.1007/978-3-540-88188-9_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88184-1
Online ISBN: 978-3-540-88188-9
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