J
Jorge Calvo-Zaragoza
Researcher at University of Alicante
Publications - 123
Citations - 1528
Jorge Calvo-Zaragoza is an academic researcher from University of Alicante. The author has contributed to research in topics: Optical music recognition & Computer science. The author has an hindex of 19, co-authored 95 publications receiving 1001 citations. Previous affiliations of Jorge Calvo-Zaragoza include McGill University & Polytechnic University of Valencia.
Papers
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A selectional auto-encoder approach for document image binarization
TL;DR: This paper discusses the use of convolutional auto-encoders devoted to learning an end-to-end map from an input image to its selectional output, in which activations indicate the likelihood of pixels to be either foreground or background.
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Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation
TL;DR: This paper takes as initial point an ASS strategy based on clustering, and improves its performance by solving issues related to instances located close to the cluster boundaries by enlarging their size and considering the use of Deep Neural Networks for learning a suitable representation for the classification task at issue.
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Improving kNN multi-label classification in Prototype Selection scenarios using class proposals
TL;DR: A new strategy for multi-label classifications tasks is proposed to solve this accuracy drop without the need of using all the training set, and is not only able to reach the performance of conventional kNN with barely a third of distances computed, but also outperform the latter in noisy scenarios, proving to be a much more robust approach.
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End-to-End Neural Optical Music Recognition of Monophonic Scores
Jorge Calvo-Zaragoza,David Rizo +1 more
TL;DR: This work studies the use of neural networks that work in an end-to-end manner by using a neural model that combines the capabilities of convolutional neural Networks, which work on the input image, and recurrent neural networks, which deal with the sequential nature of the problem.
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Understanding Optical Music Recognition
TL;DR: This work provides a robust definition of OMR and its relationship to related fields, analyzes how OMR inverts the music encoding process to recover the musical notation and the musical semantics from documents, and proposes a taxonomy of O MR, with most notably a novel taxonomic of applications.