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Dimosthenis Karatzas. 2008. Detecting Gradients in Text Images Using the Hough Transform. Proceedings of the 8th International Workshop on Document Analysis Systems,.245–252.
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Alicia Fornes, Josep Llados, Gemma Sanchez and Horst Bunke. 2008. Writer Identification in Old Handwritten Music Scores. Proceedings of the 8th International Workshop on Document Analysis Systems,.347–353.
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Partha Pratim Roy, Umapada Pal and Josep Llados. 2008. Multi-oriented English Text Line Extraction using Background and Foreground Information. Proceedings of the 8th IAPR International Workshop on Document Analysis Systems,.315–322.
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Marçal Rusiñol and Josep Llados. 2008. Word and Symbol Spotting using Spatial Organization of Local Descriptors. Proceedings of the 8th IAPR International Workshop on Document Analysis Systems,.489–496.
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T.O. Nguyen, Salvatore Tabbone and Oriol Ramos Terrades. 2008. Symbol Descriptor Based on Shape Context and Vector Model of Information Retrieval. Proceedings of the 8th IAPR International Workshop on Document Analysis Systems,.191–197.
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A. Pujol and 6 others. 1999. Real time pharmaceutical product recognition using color and shape indexing. Proceedings of the 2nd International Workshop on European Scientific and Industrial Collaboration (WESIC´99), Promotoring Advanced Technologies in Manufacturing..
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Debora Gil, Jordi Gonzalez and Gemma Sanchez, eds. 2007. Computer Vision: Advances in Research and Development. Bellaterra (Spain), UAB. (2.)
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Alicia Fornes, Volkmar Frinken, Andreas Fischer, Jon Almazan, G. Jackson and Horst Bunke. 2011. A Keyword Spotting Approach Using Blurred Shape Model-Based Descriptors. Proceedings of the 2011 Workshop on Historical Document Imaging and Processing. ACM, 83–90.
Abstract: The automatic processing of handwritten historical documents is considered a hard problem in pattern recognition. In addition to the challenges given by modern handwritten data, a lack of training data as well as effects caused by the degradation of documents can be observed. In this scenario, keyword spotting arises to be a viable solution to make documents amenable for searching and browsing. For this task we propose the adaptation of shape descriptors used in symbol recognition. By treating each word image as a shape, it can be represented using the Blurred Shape Model and the De-formable Blurred Shape Model. Experiments on the George Washington database demonstrate that this approach is able to outperform the commonly used Dynamic Time Warping approach.
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Andreas Fischer, Volkmar Frinken, Alicia Fornes and Horst Bunke. 2011. Transcription Alignment of Latin Manuscripts Using Hidden Markov Models. Proceedings of the 2011 Workshop on Historical Document Imaging and Processing. ACM, 29–36.
Abstract: Transcriptions of historical documents are a valuable source for extracting labeled handwriting images that can be used for training recognition systems. In this paper, we introduce the Saint Gall database that includes images as well as the transcription of a Latin manuscript from the 9th century written in Carolingian script. Although the available transcription is of high quality for a human reader, the spelling of the words is not accurate when compared with the handwriting image. Hence, the transcription poses several challenges for alignment regarding, e.g., line breaks, abbreviations, and capitalization. We propose an alignment system based on character Hidden Markov Models that can cope with these challenges and efficiently aligns complete document pages. On the Saint Gall database, we demonstrate that a considerable alignment accuracy can be achieved, even with weakly trained character models.
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Josep Llados, Jaime Lopez-Krahe and Enric Marti. 1996. Hand drawn document understanding using the straight line Hough transform and graph matching. Proceedings of the 13th International Pattern Recognition Conference (ICPR’96). Vienna , Austria, 497–501.
Abstract: This paper presents a system to understand hand drawn architectural drawings in a CAD environment. The procedure is to identify in a floor plan the building elements, stored in a library of patterns, and their spatial relationships. The vectorized input document and the patterns to recognize are represented by attributed graphs. To recognize the patterns as such, we apply a structural approach based on subgraph isomorphism techniques. In spite of their value, graph matching techniques do not recognize adequately those building elements characterized by hatching patterns, i.e. walls. Here we focus on the recognition of hatching patterns and develop a straight line Hough transform based method in order to detect the regions filled in with parallel straight fines. This allows not only to recognize filling patterns, but it actually reduces the computational load associated with the subgraph isomorphism computation. The result is that the document can be redrawn by editing all the patterns recognized
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