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Felipe Lumbreras and 7 others. 2001. Visual Inspection of Safety Belts. International Conference on Quality Control by Artificial Vision.526–531.
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Xavier Boix and 7 others. 2009. Combining local and global bag-of-word representations for semantic segmentation. Workshop on The PASCAL Visual Object Classes Challenge.
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Petia Radeva and Joan Serrat. 1993. Rubber Snake: Implementation on Signed Distance Potential. Vision Conference.187–194.
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Jaume Amores, N. Sebe and Petia Radeva. 2007. Class-Specific Binaryy Correlograms for Object Recognition. British Machine Vision Conference.
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A. Dupuy, Joan Serrat, Jordi Vitria and J. Pladellorens. 1991. Analysis of gammagraphic images by mathematical morphology. Pattern Recognition and image Analysis: IV Spanish Symposium of Pattern Recognition and image Analysis, World Scientific Pub..
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Joan Serrat, Jordi Vitria and J. Pladellorens. 1991. Morphological Segmentation of Heart Scintigraphic image Sequences. Computer Assisted Radiology..
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Marçal Rusiñol, David Aldavert, Ricardo Toledo and Josep Llados. 2015. Towards Query-by-Speech Handwritten Keyword Spotting. 13th International Conference on Document Analysis and Recognition ICDAR2015.501–505.
Abstract: In this paper, we present a new querying paradigm for handwritten keyword spotting. We propose to represent handwritten word images both by visual and audio representations, enabling a query-by-speech keyword spotting system. The two representations are merged together and projected to a common sub-space in the training phase. This transform allows to, given a spoken query, retrieve word instances that were only represented by the visual modality. In addition, the same method can be used backwards at no additional cost to produce a handwritten text-tospeech system. We present our first results on this new querying mechanism using synthetic voices over the George Washington
dataset.
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David Aldavert, Marçal Rusiñol and Ricardo Toledo. 2017. Automatic Static/Variable Content Separation in Administrative Document Images. 14th International Conference on Document Analysis and Recognition.
Abstract: In this paper we present an automatic method for separating static and variable content from administrative document images. An alignment approach is able to unsupervisedly build probabilistic templates from a set of examples of the same document kind. Such templates define which is the likelihood of every pixel of being either static or variable content. In the extraction step, the same alignment technique is used to match
an incoming image with the template and to locate the positions where variable fields appear. We validate our approach on the public NIST Structured Tax Forms Dataset.
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David Aldavert and Marçal Rusiñol. 2018. Manuscript text line detection and segmentation using second-order derivatives analysis. 13th IAPR International Workshop on Document Analysis Systems.293–298.
Abstract: In this paper, we explore the use of second-order derivatives to detect text lines on handwritten document images. Taking advantage that the second derivative gives a minimum response when a dark linear element over a
bright background has the same orientation as the filter, we use this operator to create a map with the local orientation and strength of putative text lines in the document. Then, we detect line segments by selecting and merging the filter responses that have a similar orientation and scale. Finally, text lines are found by merging the segments that are within the same text region. The proposed segmentation algorithm, is learning-free while showing a performance similar to the state of the art methods in publicly available datasets.
Keywords: text line detection; text line segmentation; text region detection; second-order derivatives
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David Aldavert and Marçal Rusiñol. 2018. Synthetically generated semantic codebook for Bag-of-Visual-Words based word spotting. 13th IAPR International Workshop on Document Analysis Systems.223–228.
Abstract: Word-spotting methods based on the Bag-ofVisual-Words framework have demonstrated a good retrieval performance even when used in a completely unsupervised manner. Although unsupervised approaches are suitable for
large document collections due to the cost of acquiring labeled data, these methods also present some drawbacks. For instance, having to train a suitable “codebook” for a certain dataset has a high computational cost. Therefore, in
this paper we present a database agnostic codebook which is trained from synthetic data. The aim of the proposed approach is to generate a codebook where the only information required is the type of script used in the document. The use of synthetic data also allows to easily incorporate semantic
information in the codebook generation. So, the proposed method is able to determine which set of codewords have a semantic representation of the descriptor feature space. Experimental results show that the resulting codebook attains a state-of-the-art performance while having a more compact representation.
Keywords: Word Spotting; Bag of Visual Words; Synthetic Codebook; Semantic Information
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