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Ernest Valveny, Philippe Dosch and Alicia Fornes. 2008. Report on the Third Contest on Symbol Recognition. In W. Liu, J.L., J.M. Ogier, ed. Graphics Recognition: Recent Advances and New Opportunities.321–328. (LNCS.)
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Mathieu Nicolas Delalandre, Tony Pridmore, Ernest Valveny, Herve Locteau and Eric Trupin. 2008. Building Synthetic Graphical Documents for Performance Evaluation. In W. Liu, J.L., J.M. Ogier, ed. Graphics Recognition: Recent Advances and New Opportunities.288–298. (LNCS.)
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Alicia Fornes, Sergio Escalera, Josep Llados, Gemma Sanchez and Joan Mas. 2008. Hand Drawn Symbol Recognition by Blurred Shape Model Descriptor and a Multiclass Classifier. In W. Liu, J.L., J.M. Ogier, ed. Graphics Recognition: Recent Advances and New Opportunities.30–40. (LNCS.)
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Alicia Fornes, Josep Llados and Gemma Sanchez. 2008. Old Handwritten Musical Symbol Classification by a Dynamic TimeWrapping Based Method. In W. Liu, J.L., J.M. Ogier, ed. Graphics Recognition: Recent Advances and New Opportunities.52–60. (LNCS.)
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Jose Antonio Rodriguez, Gemma Sanchez and Josep Llados. 2008. Categorization of Digital Ink Elements using Spectral Features. In W. Liu, J.L., J.M. Ogier, ed. Graphics Recognition: Recent Advances and New Opportunities. Springer–Verlag, 188–198. (LNCS.)
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Oriol Ramos Terrades, Ernest Valveny and Salvatore Tabbone. 2008. On the Combination of Ridgelets Descriptors for Symbol Recognition. Graphics Recognition: Recent Advances and New Oportunities, W. Lius, J. Llados, J.M. Ogier, LNCS 5046:104–113.
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Josep Llados, Gemma Sanchez and K. Tombre. 2002. An Error-Correction Graph Grammar to Recognize Texture Symbols..
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Partha Pratim Roy, Eduard Vazquez, Josep Llados, Ramon Baldrich and Umapada Pal. 2008. A System to Segment Text and Symbols from Color Maps. Graphics Recognition. Recent Advances and New Opportunities.245–256. (LNCS.)
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Lluis Pere de las Heras, Joan Mas, Gemma Sanchez and Ernest Valveny. 2013. Notation-invariant patch-based wall detector in architectural floor plans. Graphics Recognition. New Trends and Challenges. Springer Berlin Heidelberg, 79–88. (LNCS.)
Abstract: Architectural floor plans exhibit a large variability in notation. Therefore, segmenting and identifying the elements of any kind of plan becomes a challenging task for approaches based on grouping structural primitives obtained by vectorization. Recently, a patch-based segmentation method working at pixel level and relying on the construction of a visual vocabulary has been proposed in [1], showing its adaptability to different notations by automatically learning the visual appearance of the elements in each different notation. This paper presents an evolution of that previous work, after analyzing and testing several alternatives for each of the different steps of the method: Firstly, an automatic plan-size normalization process is done. Secondly we evaluate different features to obtain the description of every patch. Thirdly, we train an SVM classifier to obtain the category of every patch instead of constructing a visual vocabulary. These variations of the method have been tested for wall detection on two datasets of architectural floor plans with different notations. After studying in deep each of the steps in the process pipeline, we are able to find the best system configuration, which highly outperforms the results on wall segmentation obtained by the original paper.
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Arnau Baro, Pau Riba, Jorge Calvo-Zaragoza and Alicia Fornes. 2018. Optical Music Recognition by Long Short-Term Memory Networks. In A. Fornes, B.L., ed. Graphics Recognition. Current Trends and Evolutions. Springer, 81–95. (LNCS.)
Abstract: Optical Music Recognition refers to the task of transcribing the image of a music score into a machine-readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level. The experimental results are promising, showing the benefits of our approach.
Keywords: Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory
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