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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On the Influence of Word Representations for Handwritten Word Spotting in Historical Documents |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Handwriting recognition; word spotting; historical documents; feature representation; shape descriptors Read More: http://www.worldscientific.com/doi/abs/10.1142/S0218001412630025 |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advantages and disadvantages of statistical and structural models in a framework of query-by-example word spotting in historical documents.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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Admin @ si @ LRF2012 |
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2128 |
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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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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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Mohammed Al Rawi; Dimosthenis Karatzas |
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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 |
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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Alicia Fornes; Josep Llados; Gemma Sanchez; Horst Bunke |
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On the use of textural features for writer identification in old handwritten music scores |
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Conference Article |
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2009 |
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10th International Conference on Document Analysis and Recognition |
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996 - 1000 |
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Writer identification consists in determining the writer of a piece of handwriting from a set of writers. In this paper we present a system for writer identification in old handwritten music scores which uses only music notation to determine the author. The steps of the proposed system are the following. First of all, the music sheet is preprocessed for obtaining a music score without the staff lines. Afterwards, four different methods for generating texture images from music symbols are applied. Every approach uses a different spatial variation when combining the music symbols to generate the textures. Finally, Gabor filters and Grey-scale Co-ocurrence matrices are used to obtain the features. The classification is performed using a k-NN classifier based on Euclidean distance. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving encouraging identification rates. |
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Barcelona |
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1520-5363 |
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978-1-4244-4500-4 |
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DAG @ dag @ FLS2009b |
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1223 |
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Author |
N. Zakaria; Jean-Marc Ogier; Josep Llados |
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On-line Graphics Recognition based on Invariant Spatio-Sequential Descriptor: Fuzzy Matrix |
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Miscellaneous |
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2005 |
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Sixth IAPR International Workshop on Graphics Recognition (GREC 2005), 248–259 |
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Hong Kong (China) |
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DAG @ dag @ YFY2005b |
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622 |
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Mohamed Ali Souibgui; Ali Furkan Biten; Sounak Dey; Alicia Fornes; Yousri Kessentini; Lluis Gomez; Dimosthenis Karatzas; Josep Llados |
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Title |
One-shot Compositional Data Generation for Low Resource Handwritten Text Recognition |
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Conference Article |
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2022 |
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Winter Conference on Applications of Computer Vision |
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Document Analysis |
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Low resource Handwritten Text Recognition (HTR) is a hard problem due to the scarce annotated data and the very limited linguistic information (dictionaries and language models). This appears, for example, in the case of historical ciphered manuscripts, which are usually written with invented alphabets to hide the content. Thus, in this paper we address this problem through a data generation technique based on Bayesian Program Learning (BPL). Contrary to traditional generation approaches, which require a huge amount of annotated images, our method is able to generate human-like handwriting using only one sample of each symbol from the desired alphabet. After generating symbols, we create synthetic lines to train state-of-the-art HTR architectures in a segmentation free fashion. Quantitative and qualitative analyses were carried out and confirm the effectiveness of the proposed method, achieving competitive results compared to the usage of real annotated data. |
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Virtual; January 2022 |
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WACV |
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DAG; 602.230; 600.140 |
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Admin @ si @ SBD2022 |
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3615 |
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Author |
Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados |
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Title |
Ontology-Based Understanding of Architectural Drawings |
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2017 |
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International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges |
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9657 |
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75-85 |
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Graphics recognition; Floor plan analysi; Domain ontology |
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In this paper we present a knowledge base of architectural documents aiming at improving existing methods of floor plan classification and understanding. It consists of an ontological definition of the domain and the inclusion of real instances coming from both, automatically interpreted and manually labeled documents. The knowledge base has proven to be an effective tool to structure our knowledge and to easily maintain and upgrade it. Moreover, it is an appropriate means to automatically check the consistency of relational data and a convenient complement of hard-coded knowledge interpretation systems. |
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DAG; 600.121 |
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Admin @ si @ HRL2017 |
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3086 |
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Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes |
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Title |
Optical Music Recognition by Long Short-Term Memory Networks |
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2018 |
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Graphics Recognition. Current Trends and Evolutions |
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11009 |
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81-95 |
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Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory |
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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. |
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Springer |
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A. Fornes, B. Lamiroy |
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978-3-030-02283-9 |
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GREC |
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DAG; 600.097; 601.302; 601.330; 600.121 |
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Admin @ si @ BRC2018 |
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3227 |
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Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes |
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Optical Music Recognition by Recurrent Neural Networks |
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2017 |
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14th IAPR International Workshop on Graphics Recognition |
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25-26 |
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Optical Music Recognition; Recurrent Neural Network; Long Short-Term Memory |
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Optical Music Recognition is the task of transcribing 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 |
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DAG; 600.097; 601.302; 600.121 |
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Admin @ si @ BRC2017 |
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3056 |
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