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Author Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades edit   pdf
url  openurl
  Title Sparse representation over learned dictionary for symbol recognition Type Journal Article
  Year 2016 Publication Signal Processing Abbreviated Journal SP  
  Volume (down) 125 Issue Pages 36-47  
  Keywords Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points  
  Abstract In this paper we propose an original sparse vector model for symbol retrieval task. More speci cally, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols.  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.061; 600.077 Approved no  
  Call Number Admin @ si @ DTR2016 Serial 2946  
Permanent link to this record
 

 
Author Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes edit  url
openurl 
  Title From Optical Music Recognition to Handwritten Music Recognition: a Baseline Type Journal Article
  Year 2019 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume (down) 123 Issue Pages 1-8  
  Keywords  
  Abstract Optical Music Recognition (OMR) is the branch of document image analysis that aims to convert images of musical scores into a computer-readable format. Despite decades of research, the recognition of handwritten music scores, concretely the Western notation, is still an open problem, and the few existing works only focus on a specific stage of OMR. In this work, we propose a full Handwritten Music Recognition (HMR) system based on Convolutional Recurrent Neural Networks, data augmentation and transfer learning, that can serve as a baseline for the research community.  
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  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference  
  Notes DAG; 600.097; 601.302; 601.330; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ BRC2019 Serial 3275  
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Author S.K. Jemni; Mohamed Ali Souibgui; Yousri Kessentini; Alicia Fornes edit  url
openurl 
  Title Enhance to Read Better: A Multi-Task Adversarial Network for Handwritten Document Image Enhancement Type Journal Article
  Year 2022 Publication Pattern Recognition Abbreviated Journal PR  
  Volume (down) 123 Issue Pages 108370  
  Keywords  
  Abstract Handwritten document images can be highly affected by degradation for different reasons: Paper ageing, daily-life scenarios (wrinkles, dust, etc.), bad scanning process and so on. These artifacts raise many readability issues for current Handwritten Text Recognition (HTR) algorithms and severely devalue their efficiency. In this paper, we propose an end to end architecture based on Generative Adversarial Networks (GANs) to recover the degraded documents into a and form. Unlike the most well-known document binarization methods, which try to improve the visual quality of the degraded document, the proposed architecture integrates a handwritten text recognizer that promotes the generated document image to be more readable. To the best of our knowledge, this is the first work to use the text information while binarizing handwritten documents. Extensive experiments conducted on degraded Arabic and Latin handwritten documents demonstrate the usefulness of integrating the recognizer within the GAN architecture, which improves both the visual quality and the readability of the degraded document images. Moreover, we outperform the state of the art in H-DIBCO challenges, after fine tuning our pre-trained model with synthetically degraded Latin handwritten images, on this task.  
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  Area Expedition Conference  
  Notes DAG; 600.124; 600.121; 602.230 Approved no  
  Call Number Admin @ si @ JSK2022 Serial 3613  
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Author Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes edit   pdf
url  openurl
  Title Learning graph edit distance by graph neural networks Type Journal Article
  Year 2021 Publication Pattern Recognition Abbreviated Journal PR  
  Volume (down) 120 Issue Pages 108132  
  Keywords  
  Abstract The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words i.e. keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset.  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ RFL2021 Serial 3611  
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Author Dena Bazazian; Raul Gomez; Anguelos Nicolaou; Lluis Gomez; Dimosthenis Karatzas; Andrew Bagdanov edit   pdf
url  openurl
  Title Fast: Facilitated and accurate scene text proposals through fcn guided pruning Type Journal Article
  Year 2019 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume (down) 119 Issue Pages 112-120  
  Keywords  
  Abstract Class-specific text proposal algorithms can efficiently reduce the search space for possible text object locations in an image. In this paper we combine the Text Proposals algorithm with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same recall level and thus gaining a significant speed up. Our experiments demonstrate that such text proposal approaches yield significantly higher recall rates than state-of-the-art text localization techniques, while also producing better-quality localizations. Our results on the ICDAR 2015 Robust Reading Competition (Challenge 4) and the COCO-text datasets show that, when combined with strong word classifiers, this recall margin leads to state-of-the-art results in end-to-end scene text recognition.  
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  Notes DAG; 600.084; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ BGN2019 Serial 3342  
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Author Miquel Ferrer; Dimosthenis Karatzas; Ernest Valveny; I. Bardaji; Horst Bunke edit  doi
openurl 
  Title A Generic Framework for Median Graph Computation based on a Recursive Embedding Approach Type Journal Article
  Year 2011 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU  
  Volume (down) 115 Issue 7 Pages 919-928  
  Keywords Median Graph, Graph Embedding, Graph Matching, Structural Pattern Recognition  
  Abstract The median graph has been shown to be a good choice to obtain a represen- tative of a set of graphs. However, its computation is a complex problem. Recently, graph embedding into vector spaces has been proposed to obtain approximations of the median graph. The problem with such an approach is how to go from a point in the vector space back to a graph in the graph space. The main contribution of this paper is the generalization of this previ- ous method, proposing a generic recursive procedure that permits to recover the graph corresponding to a point in the vector space, introducing only the amount of approximation inherent to the use of graph matching algorithms. In order to evaluate the proposed method, we compare it with the set me- dian and with the other state-of-the-art embedding-based methods for the median graph computation. The experiments are carried out using four dif- ferent databases (one semi-artificial and three containing real-world data). Results show that with the proposed approach we can obtain better medi- ans, in terms of the sum of distances to the training graphs, than with the previous existing methods.  
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  Notes DAG Approved no  
  Call Number IAM @ iam @ FKV2011 Serial 1831  
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Author Thanh Nam Le; Muhammad Muzzamil Luqman; Anjan Dutta; Pierre Heroux; Christophe Rigaud; Clement Guerin; Pasquale Foggia; Jean Christophe Burie; Jean Marc Ogier; Josep Llados; Sebastien Adam edit  url
openurl 
  Title Subgraph spotting in graph representations of comic book images Type Journal Article
  Year 2018 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume (down) 112 Issue Pages 118-124  
  Keywords Attributed graph; Region adjacency graph; Graph matching; Graph isomorphism; Subgraph isomorphism; Subgraph spotting; Graph indexing; Graph retrieval; Query by example; Dataset and comic book images  
  Abstract Graph-based representations are the most powerful data structures for extracting, representing and preserving the structural information of underlying data. Subgraph spotting is an interesting research problem, especially for studying and investigating the structural information based content-based image retrieval (CBIR) and query by example (QBE) in image databases. In this paper we address the problem of lack of freely available ground-truthed datasets for subgraph spotting and present a new dataset for subgraph spotting in graph representations of comic book images (SSGCI) with its ground-truth and evaluation protocol. Experimental results of two state-of-the-art methods of subgraph spotting are presented on the new SSGCI dataset.  
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  Notes DAG; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ LLD2018 Serial 3150  
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Author Lei Kang; Pau Riba; Mauricio Villegas; Alicia Fornes; Marçal Rusiñol edit   pdf
url  openurl
  Title Candidate Fusion: Integrating Language Modelling into a Sequence-to-Sequence Handwritten Word Recognition Architecture Type Journal Article
  Year 2021 Publication Pattern Recognition Abbreviated Journal PR  
  Volume (down) 112 Issue Pages 107790  
  Keywords  
  Abstract Sequence-to-sequence models have recently become very popular for tackling
handwritten word recognition problems. However, how to effectively integrate an external language model into such recognizer is still a challenging
problem. The main challenge faced when training a language model is to
deal with the language model corpus which is usually different to the one
used for training the handwritten word recognition system. Thus, the bias
between both word corpora leads to incorrectness on the transcriptions, providing similar or even worse performances on the recognition task. In this
work, we introduce Candidate Fusion, a novel way to integrate an external
language model to a sequence-to-sequence architecture. Moreover, it provides suggestions from an external language knowledge, as a new input to
the sequence-to-sequence recognizer. Hence, Candidate Fusion provides two
improvements. On the one hand, the sequence-to-sequence recognizer has
the flexibility not only to combine the information from itself and the language model, but also to choose the importance of the information provided
by the language model. On the other hand, the external language model
has the ability to adapt itself to the training corpus and even learn the
most commonly errors produced from the recognizer. Finally, by conducting
comprehensive experiments, the Candidate Fusion proves to outperform the
state-of-the-art language models for handwritten word recognition tasks.
 
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  Notes DAG; 600.140; 601.302; 601.312; 600.121 Approved no  
  Call Number Admin @ si @ KRV2021 Serial 3343  
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Author Andres Mafla; Ruben Tito; Sounak Dey; Lluis Gomez; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas edit  url
openurl 
  Title Real-time Lexicon-free Scene Text Retrieval Type Journal Article
  Year 2021 Publication Pattern Recognition Abbreviated Journal PR  
  Volume (down) 110 Issue Pages 107656  
  Keywords  
  Abstract In this work, we address the task of scene text retrieval: given a text query, the system returns all images containing the queried text. The proposed model uses a single shot CNN architecture that predicts bounding boxes and builds a compact representation of spotted words. In this way, this problem can be modeled as a nearest neighbor search of the textual representation of a query over the outputs of the CNN collected from the totality of an image database. Our experiments demonstrate that the proposed model outperforms previous state-of-the-art, while offering a significant increase in processing speed and unmatched expressiveness with samples never seen at training time. Several experiments to assess the generalization capability of the model are conducted in a multilingual dataset, as well as an application of real-time text spotting in videos.  
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  Notes DAG; 600.121; 600.129; 601.338 Approved no  
  Call Number Admin @ si @ MTD2021 Serial 3493  
Permanent link to this record
 

 
Author Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta edit  url
openurl 
  Title Large-scale graph indexing using binary embeddings of node contexts for information spotting in document image databases Type Journal Article
  Year 2017 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume (down) 87 Issue Pages 203-211  
  Keywords  
  Abstract Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations. However, retrieving a query graph from a large dataset of graphs implies a high computational complexity. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. With this aim, in this paper we propose a graph indexation formalism applied to visual retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Then, each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in different real scenarios such as handwritten word spotting in images of historical documents or symbol spotting in architectural floor plans.  
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  Notes DAG; 600.097; 602.006; 603.053; 600.121 Approved no  
  Call Number RLF2017b Serial 2873  
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