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Author |
Miquel Ferrer; F. Serratosa; Ernest Valveny |
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On the Relation Between the Median Graph and the Maximum Common Subgraph of a Set of Graphs |
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2007 |
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Alicante (Spain) |
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DAG @ dag @ FSV2007 |
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790 |
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Mohammed Al Rawi; Dimosthenis Karatzas |
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Title |
On the Labeling Correctness in Computer Vision Datasets |
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2018 |
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Proceedings of the Workshop on Interactive Adaptive Learning, co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases |
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Image datasets have heavily been used to build computer vision systems.
These datasets are either manually or automatically labeled, which is a
problem as both labeling methods are prone to errors. To investigate this problem, we use a majority voting ensemble that combines the results from several Convolutional Neural Networks (CNNs). Majority voting ensembles not only enhance the overall performance, but can also be used to estimate the confidence level of each sample. We also examined Softmax as another form to estimate posterior probability. We have designed various experiments with a range of different ensembles built from one or different, or temporal/snapshot CNNs, which have been trained multiple times stochastically. We analyzed CIFAR10, CIFAR100, EMNIST, and SVHN datasets and we found quite a few incorrect
labels, both in the training and testing sets. We also present detailed confidence analysis on these datasets and we found that the ensemble is better than the Softmax when used estimate the per-sample confidence. This work thus proposes an approach that can be used to scrutinize and verify the labeling of computer vision datasets, which can later be applied to weakly/semi-supervised learning. We propose a measure, based on the Odds-Ratio, to quantify how many of these incorrectly classified labels are actually incorrectly labeled and how many of these are confusing. The proposed methods are easily scalable to larger datasets, like ImageNet, LSUN and SUN, as each CNN instance is trained for 60 epochs; or even faster, by implementing a temporal (snapshot) ensemble. |
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ECML-PKDDW |
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DAG; 600.121; 600.129 |
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Admin @ si @ RaK2018 |
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3144 |
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Author |
Pau Torras; Arnau Baro; Lei Kang; Alicia Fornes |
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Title |
On the Integration of Language Models into Sequence to Sequence Architectures for Handwritten Music Recognition |
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Conference Article |
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2021 |
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International Society for Music Information Retrieval Conference |
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690-696 |
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Despite the latest advances in Deep Learning, the recognition of handwritten music scores is still a challenging endeavour. Even though the recent Sequence to Sequence(Seq2Seq) architectures have demonstrated its capacity to reliably recognise handwritten text, their performance is still far from satisfactory when applied to historical handwritten scores. Indeed, the ambiguous nature of handwriting, the non-standard musical notation employed by composers of the time and the decaying state of old paper make these scores remarkably difficult to read, sometimes even by trained humans. Thus, in this work we explore the incorporation of language models into a Seq2Seq-based architecture to try to improve transcriptions where the aforementioned unclear writing produces statistically unsound mistakes, which as far as we know, has never been attempted for this field of research on this architecture. After studying various Language Model integration techniques, the experimental evaluation on historical handwritten music scores shows a significant improvement over the state of the art, showing that this is a promising research direction for dealing with such difficult manuscripts. |
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Virtual; November 2021 |
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ISMIR |
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DAG; 600.140; 600.121 |
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Admin @ si @ TBK2021 |
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3616 |
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Author |
Josep Llados; Marçal Rusiñol; Alicia Fornes; David Fernandez; Anjan Dutta |
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Title |
On the Influence of Word Representations for Handwritten Word Spotting in Historical Documents |
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2012 |
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International Journal of Pattern Recognition and Artificial Intelligence |
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IJPRAI |
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26 |
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5 |
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1263002-126027 |
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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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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. |
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DAG |
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Admin @ si @ LRF2012 |
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2128 |
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Author |
David Fernandez; Pau Riba; Alicia Fornes; Josep Llados |
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Title |
On the Influence of Key Point Encoding for Handwritten Word Spotting |
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Conference Article |
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2014 |
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14th International Conference on Frontiers in Handwriting Recognition |
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476 - 481 |
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Local descriptors; Interest points; Handwritten documents; Word spotting; Historical document analysis |
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In this paper we evaluate the influence of the selection of key points and the associated features in the performance of word spotting processes. In general, features can be extracted from a number of characteristic points like corners, contours, skeletons, maxima, minima, crossings, etc. A number of descriptors exist in the literature using different interest point detectors. But the intrinsic variability of handwriting vary strongly on the performance if the interest points are not stable enough. In this paper, we analyze the performance of different descriptors for local interest points. As benchmarking dataset we have used the Barcelona Marriage Database that contains handwritten records of marriages over five centuries. |
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Creete Island; Grecia; September 2014 |
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2167-6445 |
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978-1-4799-4335-7 |
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ICFHR |
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DAG; 600.056; 600.061; 602.006; 600.077 |
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Admin @ si @ FRF2014 |
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2460 |
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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke; Alicia Fornes |
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Title |
On the Correlation of Graph Edit Distance and L1 Distance in the Attribute Statistics Embedding Space |
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Conference Article |
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2012 |
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Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop |
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7626 |
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135-143 |
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Graph embeddings in vector spaces aim at assigning a pattern vector to every graph so that the problems of graph classification and clustering can be solved by using data processing algorithms originally developed for statistical feature vectors. An important requirement graph features should fulfil is that they reproduce as much as possible the properties among objects in the graph domain. In particular, it is usually desired that distances between pairs of graphs in the graph domain closely resemble those between their corresponding vectorial representations. In this work, we analyse relations between the edit distance in the graph domain and the L1 distance of the attribute statistics based embedding, for which good classification performance has been reported on various datasets. We show that there is actually a high correlation between the two kinds of distances provided that the corresponding parameter values that account for balancing the weight between node and edge based features are properly selected. |
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Springer-Berlag, Berlin |
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978-3-642-34165-6 |
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SSPR&SPR |
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Admin @ si @ GVB2012c |
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2167 |
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Author |
Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone |
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Title |
On the Combination of Ridgelets Descriptors for Symbol Recognition |
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Conference Article |
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2007 |
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Seventh IAPR International Workshop on Graphics Recognition |
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18–20 |
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Curitiba (Brazil) |
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GREC |
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DAG @ dag @ RVT2007 |
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886 |
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Author |
Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone |
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On the Combination of Ridgelets Descriptors for Symbol Recognition |
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2008 |
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Graphics Recognition: Recent Advances and New Oportunities, W. Lius, J. Llados, J.M. Ogier, LNCS 5046:104–113 |
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DAG @ dag @ RVT2008 |
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984 |
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Author |
David Fernandez; Josep Llados; Alicia Fornes; R.Manmatha |
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Title |
On Influence of Line Segmentation in Efficient Word Segmentation in Old Manuscripts |
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Conference Article |
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2012 |
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13th International Conference on Frontiers in Handwriting Recognition |
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763-768 |
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document image processing;handwritten character recognition;history;image segmentation;Spanish document;historical document;line segmentation;old handwritten document;old manuscript;word segmentation;Bifurcation;Dynamic programming;Handwriting recognition;Image segmentation;Measurement;Noise;Skeleton;Segmentation;document analysis;document and text processing;handwriting analysis;heuristics;path-finding |
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Abstract |
he objective of this work is to show the importance of a good line segmentation to obtain better results in the segmentation of words of historical documents. We have used the approach developed by Manmatha and Rothfeder [1] to segment words in old handwritten documents. In their work the lines of the documents are extracted using projections. In this work, we have developed an approach to segment lines more efficiently. The new line segmentation algorithm tackles with skewed, touching and noisy lines, so it is significantly improves word segmentation. Experiments using Spanish documents from the Marriages Database of the Barcelona Cathedral show that this approach reduces the error rate by more than 20% |
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978-1-4673-2262-1 |
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ICFHR |
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Admin @ si @ FLF2012 |
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2200 |
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Author |
Alicia Fornes; Josep Llados; Gemma Sanchez |
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Title |
Old Handwritten Musical Symbol Classification by a Dynamic TimeWrapping Based Method |
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2008 |
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Graphics Recognition: Recent Advances and New Opportunities |
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5046 |
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52–60 |
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W. Liu, J. Llados, J.M. Ogier |
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DAG @ dag @ FLS2008a |
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990 |
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