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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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Miquel Ferrer, F. Serratosa and Ernest Valveny. 2007. On the Relation Between the Median Graph and the Maximum Common Subgraph of a Set of Graphs.
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Josep Llados, W. Liu and Jean-Marc Ogier. 2007. Seventh IAPR International Workshop on Graphics Recognition GREC 2007.
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Gemma Sanchez, Alicia Fornes, Joan Mas and Josep Llados. 2007. Computer Vision Tools for Visually Impaired Children Learning.
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Gemma Sanchez, Alicia Fornes, Joan Mas and Josep Llados. 2007. Computer Vision Tools for Visually Impaired Children Learning.
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Alfons Juan-Ciscar and Gemma Sanchez. 2008. PRIS 2008. Pattern Recognition in Information Systems. Proceedings of the 8th international Workshop on Pattern Recognition in Information systems – PRIS 2008, in conjunction with ICEIS 2008.
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Ernest Valveny and Enric Marti. 1997. Dimensions analysis in hand-drawn architectural drawings. (SNRFAI’97) 7th Spanish National Symposium on Pattern Recognition and Image Analysis. CVC-UAB, 90–91.
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Partha Pratim Roy, Umapada Pal and Josep Llados. 2010. Seal Object Detection in Document Images using GHT of Local Component Shapes. 10th ACM Symposium On Applied Computing.23–27.
Abstract: Due to noise, overlapped text/signature and multi-oriented nature, seal (stamp) object detection involves a difficult challenge. This paper deals with automatic detection of seal from documents with cluttered background. Here, a seal object is characterized by scale and rotation invariant spatial feature descriptors (distance and angular position) computed from recognition result of individual connected components (characters). Recognition of multi-scale and multi-oriented component is done using Support Vector Machine classifier. Generalized Hough Transform (GHT) is used to detect the seal and a voting is casted for finding possible location of the seal object in a document based on these spatial feature descriptor of components pairs. The peak of votes in GHT accumulator validates the hypothesis to locate the seal object in a document. Experimental results show that, the method is efficient to locate seal instance of arbitrary shape and orientation in documents.
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Oriol Ramos Terrades, Alejandro Hector Toselli, Nicolas Serrano, Veronica Romero, Enrique Vidal and Alfons Juan. 2010. Interactive layout analysis and transcription systems for historic handwritten documents. 10th ACM Symposium on Document Engineering.219–222.
Abstract: The amount of digitized legacy documents has been rising dramatically over the last years due mainly to the increasing number of on-line digital libraries publishing this kind of documents, waiting to be classified and finally transcribed into a textual electronic format (such as ASCII or PDF). Nevertheless, most of the available fully-automatic applications addressing this task are far from being perfect and heavy and inefficient human intervention is often required to check and correct the results of such systems. In contrast, multimodal interactive-predictive approaches may allow the users to participate in the process helping the system to improve the overall performance. With this in mind, two sets of recent advances are introduced in this work: a novel interactive method for text block detection and two multimodal interactive handwritten text transcription systems which use active learning and interactive-predictive technologies in the recognition process.
Keywords: Handwriting recognition; Interactive predictive processing; Partial supervision; Interactive layout analysis
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Albert Gordo, Florent Perronnin and Ernest Valveny. 2012. Document classification using multiple views. 10th IAPR International Workshop on Document Analysis Systems. IEEE Computer Society Washington, 33–37.
Abstract: The combination of multiple features or views when representing documents or other kinds of objects usually leads to improved results in classification (and retrieval) tasks. Most systems assume that those views will be available both at training and test time. However, some views may be too `expensive' to be available at test time. In this paper, we consider the use of Canonical Correlation Analysis to leverage `expensive' views that are available only at training time. Experimental results show that this information may significantly improve the results in a classification task.
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