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Albert Gordo. 2009. A Cyclic Page Layout Descriptor for Document Classification & Retrieval. (Master's thesis, .)
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Albert Gordo. 2013. Document Image Representation, Classification and Retrieval in Large-Scale Domains. (Ph.D. thesis, Ediciones Graficas Rey.)
Abstract: Despite the “paperless office” ideal that started in the decade of the seventies, businesses still strive against an increasing amount of paper documentation. Companies still receive huge amounts of paper documentation that need to be analyzed and processed, mostly in a manual way. A solution for this task consists in, first, automatically scanning the incoming documents. Then, document images can be analyzed and information can be extracted from the data. Documents can also be automatically dispatched to the appropriate workflows, used to retrieve similar documents in the dataset to transfer information, etc.
Due to the nature of this “digital mailroom”, we need document representation methods to be general, i.e., able to cope with very different types of documents. We need the methods to be sound, i.e., able to cope with unexpected types of documents, noise, etc. And, we need to methods to be scalable, i.e., able to cope with thousands or millions of documents that need to be processed, stored, and consulted. Unfortunately, current techniques of document representation, classification and retrieval are not apt for this digital mailroom framework, since they do not fulfill some or all of these requirements.
Through this thesis we focus on the problem of document representation aimed at classification and retrieval tasks under this digital mailroom framework. We first propose a novel document representation based on runlength histograms, and extend it to cope with more complex documents such as multiple-page documents, or documents that contain more sources of information such as extracted OCR text. Then we focus on the scalability requirements and propose a novel binarization method which we dubbed PCAE, as well as two general asymmetric distances between binary embeddings that can significantly improve the retrieval results at a minimal extra computational cost. Finally, we note the importance of supervised learning when performing large-scale retrieval, and study several approaches that can significantly boost the results at no extra cost at query time.
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Jean-Marc Ogier, Wenyin Liu and Josep Llados, eds. 2010. Graphics Recognition: Achievements, Challenges, and Evolution. Springer Link. (LNCS.)
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Marçal Rusiñol, R.Roset, Josep Llados and C.Montaner. 2011. Automatic Index Generation of Digitized Map Series by Coordinate Extraction and Interpretation. In Proceedings of the Sixth International Workshop on Digital Technologies in Cartographic Heritage.
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Dimosthenis Karatzas and Ch. Lioutas. 1998. Software Package Development for Electron Diffraction Image Analysis. Proceedings of the XIV Solid State Physics National Conference.
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Volkmar Frinken, Francisco Zamora, Salvador España, Maria Jose Castro, Andreas Fischer and Horst Bunke. 2012. Long-Short Term Memory Neural Networks Language Modeling for Handwriting Recognition. 21st International Conference on Pattern Recognition.701–704.
Abstract: Unconstrained handwritten text recognition systems maximize the combination of two separate probability scores. The first one is the observation probability that indicates how well the returned word sequence matches the input image. The second score is the probability that reflects how likely a word sequence is according to a language model. Current state-of-the-art recognition systems use statistical language models in form of bigram word probabilities. This paper proposes to model the target language by means of a recurrent neural network with long-short term memory cells. Because the network is recurrent, the considered context is not limited to a fixed size especially as the memory cells are designed to deal with long-term dependencies. In a set of experiments conducted on the IAM off-line database we show the superiority of the proposed language model over statistical n-gram models.
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Marçal Rusiñol, Dimosthenis Karatzas, Andrew Bagdanov and Josep Llados. 2012. Multipage Document Retrieval by Textual and Visual Representations. 21st International Conference on Pattern Recognition.521–524.
Abstract: In this paper we present a multipage administrative document image retrieval system based on textual and visual representations of document pages. Individual pages are represented by textual or visual information using a bag-of-words framework. Different fusion strategies are evaluated which allow the system to perform multipage document retrieval on the basis of a single page retrieval system. Results are reported on a large dataset of document images sampled from a banking workflow.
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Volkmar Frinken, Markus Baumgartner, Andreas Fischer and Horst Bunke. 2012. Semi-Supervised Learning for Cursive Handwriting Recognition using Keyword Spotting. 13th International Conference on Frontiers in Handwriting Recognition.49–54.
Abstract: State-of-the-art handwriting recognition systems are learning-based systems that require large sets of training data. The creation of training data, and consequently the creation of a well-performing recognition system, requires therefore a substantial amount of human work. This can be reduced with semi-supervised learning, which uses unlabeled text lines for training as well. Current approaches estimate the correct transcription of the unlabeled data via handwriting recognition which is not only extremely demanding as far as computational costs are concerned but also requires a good model of the target language. In this paper, we propose a different approach that makes use of keyword spotting, which is significantly faster and does not need any language model. In a set of experiments we demonstrate its superiority over existing approaches.
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Marçal Rusiñol and 7 others. 2012. CVC-UAB's participation in the Flowchart Recognition Task of CLEF-IP 2012. Conference and Labs of the Evaluation Forum.
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Christophe Rigaud, Dimosthenis Karatzas, Joost Van de Weijer, Jean-Christophe Burie and Jean-Marc Ogier. 2013. Automatic text localisation in scanned comic books. Proceedings of the International Conference on Computer Vision Theory and Applications.814–819.
Abstract: Comic books constitute an important cultural heritage asset in many countries. Digitization combined with subsequent document understanding enable direct content-based search as opposed to metadata only search (e.g. album title or author name). Few studies have been done in this direction. In this work we detail a novel approach for the automatic text localization in scanned comics book pages, an essential step towards a fully automatic comics book understanding. We focus on speech text as it is semantically important and represents the majority of the text present in comics. The approach is compared with existing methods of text localization found in the literature and results are presented.
Keywords: Text localization; comics; text/graphic separation; complex background; unstructured document
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