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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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Francisco Cruz and Oriol Ramos Terrades. 2012. Document segmentation using relative location features. 21st International Conference on Pattern Recognition.1562–1565.
Abstract: In this paper we evaluate the use of Relative Location Features (RLF) on a historical document segmentation task, and compare the quality of the results obtained on structured and unstructured documents using RLF and not using them. We prove that using these features improve the final segmentation on documents with a strong structure, while their application on unstructured documents does not show significant improvement. Although this paper is not focused on segmenting unstructured documents, results obtained on a benchmark dataset are equal or even overcome previous results of similar works.
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Marçal Rusiñol and Josep Llados. 2012. The Role of the Users in Handwritten Word Spotting Applications: Query Fusion and Relevance Feedback. 13th International Conference on Frontiers in Handwriting Recognition.55–60.
Abstract: In this paper we present the importance of including the user in the loop in a handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and a baseline word spotting approach based on a bag-of-visual-words model.
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Emanuel Indermühle, Volkmar Frinken and Horst Bunke. 2012. Mode Detection in Online Handwritten Documents using BLSTM Neural Networks. 13th International Conference on Frontiers in Handwriting Recognition.302–307.
Abstract: Mode detection in online handwritten documents refers to the process of distinguishing different types of contents, such as text, formulas, diagrams, or tables, one from another. In this paper a new approach to mode detection is proposed that uses bidirectional long-short term memory (BLSTM) neural networks. The BLSTM neural network is a novel type of recursive neural network that has been successfully applied in speech and handwriting recognition. In this paper we show that it has the potential to significantly outperform traditional methods for mode detection, which are usually based on stroke classification. As a further advantage over previous approaches, the proposed system is trainable and does not rely on user-defined heuristics. Moreover, it can be easily adapted to new or additional types of modes by just providing the system with new training data.
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Ernest Valveny, Robert Benavente, Agata Lapedriza, Miquel Ferrer, Jaume Garcia and Gemma Sanchez. 2012. Adaptation of a computer programming course to the EXHE requirements: evaluation five years later.
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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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Rahat Khan, Joost Van de Weijer, Dimosthenis Karatzas and Damien Muselet. 2013. Towards multispectral data acquisition with hand-held devices. 20th IEEE International Conference on Image Processing.2053–2057.
Abstract: We propose a method to acquire multispectral data with handheld devices with front-mounted RGB cameras. We propose to use the display of the device as an illuminant while the camera captures images illuminated by the red, green and
blue primaries of the display. Three illuminants and three response functions of the camera lead to nine response values which are used for reflectance estimation. Results are promising and show that the accuracy of the spectral reconstruction improves in the range from 30-40% over the spectral
reconstruction based on a single illuminant. Furthermore, we propose to compute sensor-illuminant aware linear basis by discarding the part of the reflectances that falls in the sensorilluminant null-space. We show experimentally that optimizing reflectance estimation on these new basis functions decreases
the RMSE significantly over basis functions that are independent to sensor-illuminant. We conclude that, multispectral data acquisition is potentially possible with consumer hand-held devices such as tablets, mobiles, and laptops, opening up applications which are currently considered to be unrealistic.
Keywords: Multispectral; mobile devices; color measurements
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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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Josep Llados, Marçal Rusiñol, Alicia Fornes, David Fernandez and Anjan Dutta. 2012. On the Influence of Word Representations for Handwritten Word Spotting in Historical Documents. IJPRAI, 26(5), 1263002–126027.
Abstract: 0,624 JCR
Word spotting is the process of retrieving all instances of a queried keyword from a digital library of document images. In this paper we evaluate the performance of different word descriptors to assess the advantages and disadvantages of statistical and structural models in a framework of query-by-example word spotting in historical documents. We compare four word representation models, namely sequence alignment using DTW as a baseline reference, a bag of visual words approach as statistical model, a pseudo-structural model based on a Loci features representation, and a structural approach where words are represented by graphs. The four approaches have been tested with two collections of historical data: the George Washington database and the marriage records from the Barcelona Cathedral. We experimentally demonstrate that statistical representations generally give a better performance, however it cannot be neglected that large descriptors are difficult to be implemented in a retrieval scenario where word spotting requires the indexation of data with million word images.
Keywords: Handwriting recognition; word spotting; historical documents; feature representation; shape descriptors Read More: http://www.worldscientific.com/doi/abs/10.1142/S0218001412630025
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Thanh Ha Do, Salvatore Tabbone and Oriol Ramos Terrades. 2012. Text/graphic separation using a sparse representation with multi-learned dictionaries. 21st International Conference on Pattern Recognition.
Abstract: In this paper, we propose a new approach to extract text regions from graphical documents. In our method, we first empirically construct two sequences of learned dictionaries for the text and graphical parts respectively. Then, we compute the sparse representations of all different sizes and non-overlapped document patches in these learned dictionaries. Based on these representations, each patch can be classified into the text or graphic category by comparing its reconstruction errors. Same-sized patches in one category are then merged together to define the corresponding text or graphic layers which are combined to createfinal text/graphic layer. Finally, in a post-processing step, text regions are further filtered out by using some learned thresholds.
Keywords: Graphics Recognition; Layout Analysis; Document Understandin
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