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Author Manuel Carbonell; Alicia Fornes; Mauricio Villegas; Josep Llados edit   pdf
openurl 
  Title A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages Type Journal Article
  Year (down) 2020 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 136 Issue Pages 219-227  
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  Abstract In the last years, the consolidation of deep neural network architectures for information extraction in document images has brought big improvements in the performance of each of the tasks involved in this process, consisting of text localization, transcription, and named entity recognition. However, this process is traditionally performed with separate methods for each task. In this work we propose an end-to-end model that combines a one stage object detection network with branches for the recognition of text and named entities respectively in a way that shared features can be learned simultaneously from the training error of each of the tasks. By doing so the model jointly performs handwritten text detection, transcription, and named entity recognition at page level with a single feed forward step. We exhaustively evaluate our approach on different datasets, discussing its advantages and limitations compared to sequential approaches. The results show that the model is capable of benefiting from shared features by simultaneously solving interdependent tasks.  
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  Notes DAG; 600.140; 601.311; 600.121 Approved no  
  Call Number Admin @ si @ CFV2020 Serial 3451  
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Author Pau Riba; Josep Llados; Alicia Fornes edit  url
openurl 
  Title Hierarchical graphs for coarse-to-fine error tolerant matching Type Journal Article
  Year (down) 2020 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 134 Issue Pages 116-124  
  Keywords Hierarchical graph representation; Coarse-to-fine graph matching; Graph-based retrieval  
  Abstract During the last years, graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their ability to capture both structural and appearance-based information. Thus, they provide a greater representational power than classical statistical frameworks. However, graph-based representations leads to high computational complexities usually dealt by graph embeddings or approximated matching techniques. Despite their representational power, they are very sensitive to noise and small variations of the input image. With the aim to cope with the time complexity and the variability present in the generated graphs, in this paper we propose to construct a novel hierarchical graph representation. Graph clustering techniques adapted from social media analysis have been used in order to contract a graph at different abstraction levels while keeping information about the topology. Abstract nodes attributes summarise information about the contracted graph partition. For the proposed representations, a coarse-to-fine matching technique is defined. Hence, small graphs are used as a filtering before more accurate matching methods are applied. This approach has been validated in real scenarios such as classification of colour images or retrieval of handwritten words (i.e. word spotting).  
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  Notes DAG; 600.097; 601.302; 603.057; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ RLF2020 Serial 3349  
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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 (down) 2019 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 123 Issue Pages 1-8  
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  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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  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 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 (down) 2019 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 119 Issue Pages 112-120  
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  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 Juan Ignacio Toledo; Manuel Carbonell; Alicia Fornes; Josep Llados edit  url
openurl 
  Title Information Extraction from Historical Handwritten Document Images with a Context-aware Neural Model Type Journal Article
  Year (down) 2019 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 86 Issue Pages 27-36  
  Keywords Document image analysis; Handwritten documents; Named entity recognition; Deep neural networks  
  Abstract Many historical manuscripts that hold trustworthy memories of the past societies contain information organized in a structured layout (e.g. census, birth or marriage records). The precious information stored in these documents cannot be effectively used nor accessed without costly annotation efforts. The transcription driven by the semantic categories of words is crucial for the subsequent access. In this paper we describe an approach to extract information from structured historical handwritten text images and build a knowledge representation for the extraction of meaning out of historical data. The method extracts information, such as named entities, without the need of an intermediate transcription step, thanks to the incorporation of context information through language models. Our system has two variants, the first one is based on bigrams, whereas the second one is based on recurrent neural networks. Concretely, our second architecture integrates a Convolutional Neural Network to model visual information from word images together with a Bidirecitonal Long Short Term Memory network to model the relation among the words. This integrated sequential approach is able to extract more information than just the semantic category (e.g. a semantic category can be associated to a person in a record). Our system is generic, it deals with out-of-vocabulary words by design, and it can be applied to structured handwritten texts from different domains. The method has been validated with the ICDAR IEHHR competition protocol, outperforming the existing approaches.  
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  Notes DAG; 600.097; 601.311; 603.057; 600.084; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ TCF2019 Serial 3166  
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