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Alicia Fornes, Josep Llados and Gemma Sanchez. 2006. Primitive Segmentation in Old Handwritten Music Scores. Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 288–299.
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Alicia Fornes, Josep Llados and Gemma Sanchez. 2005. Staff and graphical primitive segmentation in old handwritten music scores.
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Alicia Fornes, Josep Llados and Gemma Sanchez. 2005. Primitive Segmentation in Old Handwritten Music Scores.
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Miquel Ferrer, Dimosthenis Karatzas, Ernest Valveny and Horst Bunke. 2009. A Recursive Embedding Approach to Median Graph Computation. 7th IAPR – TC–15 Workshop on Graph–Based Representations in Pattern Recognition. Springer Berlin Heidelberg, 113–123. (LNCS.)
Abstract: The median graph has been shown to be a good choice to infer a representative of a set of graphs. It has been successfully applied to graph-based classification and clustering. Nevertheless, its computation is extremely complex. Several approaches have been presented up to now based on different strategies. In this paper we present a new approximate recursive algorithm for median graph computation based on graph embedding into vector spaces. Preliminary experiments on three databases show that this new approach is able to obtain better medians than the previous existing approaches.
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Miquel Ferrer and Ernest Valveny. 2007. Combination of OCR Engines for Page Segmentation based on Performance Evaluation. 9th International Conference on Document Analysis and Recognition.784–788.
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Mathieu Nicolas Delalandre, Ernest Valveny, Tony Pridmore and Dimosthenis Karatzas. 2010. Generation of Synthetic Documents for Performance Evaluation of Symbol Recognition & Spotting Systems. IJDAR, 13(3), 187–207.
Abstract: This paper deals with the topic of performance evaluation of symbol recognition & spotting systems. We propose here a new approach to the generation of synthetic graphics documents containing non-isolated symbols in a real context. This approach is based on the definition of a set of constraints that permit us to place the symbols on a pre-defined background according to the properties of a particular domain (architecture, electronics, engineering, etc.). In this way, we can obtain a large amount of images resembling real documents by simply defining the set of constraints and providing a few pre-defined backgrounds. As documents are synthetically generated, the groundtruth (the location and the label of every symbol) becomes automatically available. We have applied this approach to the generation of a large database of architectural drawings and electronic diagrams, which shows the flexibility of the system. Performance evaluation experiments of a symbol localization system show that our approach permits to generate documents with different features that are reflected in variation of localization results.
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Mathieu Nicolas Delalandre, Ernest Valveny and Josep Llados. 2008. Performance Evaluation of Symbol Recognition and Spotting Systems. Proceedings of the 8th International Workshop on Document Analysis Systems,.497–505.
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Mathieu Nicolas Delalandre, Ernest Valveny and Josep Llados. 2008. Performance Evaluation of Symbol Recognition and Spotting Systems: An Overview.
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Mathieu Nicolas Delalandre, Jean-Yves Ramel, Ernest Valveny and Muhammad Muzzamil Luqman. 2009. A Performance Characterization Algorithm for Symbol Localization. 8th IAPR International Workshop on Graphics Recognition. Springer, 3–11.
Abstract: In this paper we present an algorithm for performance characterization of symbol localization systems. This algorithm is aimed to be a more “reliable” and “open” solution to characterize the performance. To achieve that, it exploits only single points as the result of localization and offers the possibility to reconsider the localization results provided by a system. We use the information about context in groundtruth, and overall localization results, to detect the ambiguous localization results. A probability score is computed for each matching between a localization point and a groundtruth region, depending on the spatial distribution of the other regions in the groundtruth. Final characterization is given with detection rate/probability score plots, describing the sets of possible interpretations of the localization results, according to a given confidence rate. We present experimentation details along with the results for the symbol localization system of [1], exploiting a synthetic dataset of architectural floorplans and electrical diagrams (composed of 200 images and 3861 symbols).
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Mathieu Nicolas Delalandre, Tony Pridmore, Ernest Valveny, Herve Locteau and Eric Trupin. 2008. Building Synthetic Graphical Documents for Performance Evaluation. In W. Liu, J.L., J.M. Ogier, ed. Graphics Recognition: Recent Advances and New Opportunities.288–298. (LNCS.)
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