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Author Fernando Vilariño edit  openurl
  Title Unveiling the Social Impact of AI Type Conference Article
  Year 2020 Publication Workshop at Digital Living Lab Days Conference Abbreviated Journal  
  Volume Issue Pages  
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  Abstract  
  Address September 2020  
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  Area Expedition Conference  
  Notes MV; DAG; 600.121; 600.140;SIAI Approved no  
  Call Number Admin @ si @ Vil2020 Serial (down) 3459  
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Author Fernando Vilariño edit  openurl
  Title Public Libraries Exploring how technology transforms the cultural experience of people Type Conference Article
  Year 2019 Publication Workshop on Social Impact of AI. Open Living Lab Days Conference. Abbreviated Journal  
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  Abstract  
  Address Thessaloniki; Grecia; September 2019  
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  Notes MV; DAG; 600.140; 600.121;SIAI Approved no  
  Call Number Admin @ si @ Vil2019b Serial (down) 3458  
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Author Fernando Vilariño edit  openurl
  Title Library Living Lab, Numérisation 3D des chapiteaux du cloître de Saint-Cugat : des citoyens co- créant le nouveau patrimoine culturel numérique Type Conference Article
  Year 2019 Publication Intersectorialité et approche Living Labs. Entretiens Jacques-Cartier Abbreviated Journal  
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  Address Montreal; Canada; December 2019  
  Corporate Author Thesis  
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  ISSN ISBN Medium  
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  Notes MV; DAG; 600.140; 600.121;SIAI Approved no  
  Call Number Admin @ si @ Vil2019a Serial (down) 3457  
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Author Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero edit  url
doi  openurl
  Title Recurrent Comparator with attention models to detect counterfeit documents Type Conference Article
  Year 2019 Publication 15th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages  
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  Abstract This paper is focused on the detection of counterfeit documents via the recurrent comparison of the security textured background regions of two images. The main contributions are twofold: first we apply and adapt a recurrent comparator architecture with attention mechanism to the counterfeit detection task, which constructs a representation of the background regions by recurrently condition the next observation, learning the difference between genuine and counterfeit images through iterative glimpses. Second we propose a new counterfeit document dataset to ensure the generalization of the learned model towards the detection of the lack of resolution during the counterfeit manufacturing. The presented network, outperforms state-of-the-art classification approaches for counterfeit detection as demonstrated in the evaluation.  
  Address Sidney; Australia; September 2019  
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  Publisher Place of Publication Editor  
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  Area Expedition Conference ICDAR  
  Notes DAG; 600.140; 600.121; 601.269 Approved no  
  Call Number Admin @ si @ BRL2019 Serial (down) 3456  
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Author Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal edit   pdf
url  openurl
  Title Evaluation of the Effect of Improper Segmentation on Word Spotting Type Journal Article
  Year 2019 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR  
  Volume 22 Issue Pages 361-374  
  Keywords  
  Abstract Word spotting is an important recognition task in large-scale retrieval of document collections. In most of the cases, methods are developed and evaluated assuming perfect word segmentation. In this paper, we propose an experimental framework to quantify the goodness that word segmentation has on the performance achieved by word spotting methods in identical unbiased conditions. The framework consists of generating systematic distortions on segmentation and retrieving the original queries from the distorted dataset. We have tested our framework on several established and state-of-the-art methods using George Washington and Barcelona Marriage Datasets. The experiments done allow for an estimate of the end-to-end performance of word spotting methods.  
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  Notes DAG; 600.097; 600.084; 600.121; 600.140; 600.129 Approved no  
  Call Number Admin @ si @ DNL2019 Serial (down) 3455  
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Author Mohamed Ali Souibgui; Y.Kessentini edit   pdf
url  doi
openurl 
  Title DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement Type Journal Article
  Year 2022 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 44 Issue 3 Pages 1180-1191  
  Keywords  
  Abstract Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named Document Enhancement Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs) to restore severely degraded document images. To the best of our knowledge, this practice has not been studied within the context of generative adversarial deep networks. We demonstrate that, in different tasks (document clean up, binarization, deblurring and watermark removal), DE-GAN can produce an enhanced version of the degraded document with a high quality. In addition, our approach provides consistent improvements compared to state-of-the-art methods over the widely used DIBCO 2013, DIBCO 2017 and H-DIBCO 2018 datasets, proving its ability to restore a degraded document image to its ideal condition. The obtained results on a wide variety of degradation reveal the flexibility of the proposed model to be exploited in other document enhancement problems.  
  Address 1 March 2022  
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  Notes DAG; 602.230; 600.121; 600.140 Approved no  
  Call Number Admin @ si @ SoK2022 Serial (down) 3454  
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Author B. Gautam; Oriol Ramos Terrades; Joana Maria Pujadas-Mora; Miquel Valls-Figols edit   pdf
url  openurl
  Title Knowledge graph based methods for record linkage Type Journal Article
  Year 2020 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 136 Issue Pages 127-133  
  Keywords  
  Abstract Nowadays, it is common in Historical Demography the use of individual-level data as a consequence of a predominant life-course approach for the understanding of the demographic behaviour, family transition, mobility, etc. Advanced record linkage is key since it allows increasing the data complexity and its volume to be analyzed. However, current methods are constrained to link data from the same kind of sources. Knowledge graph are flexible semantic representations, which allow to encode data variability and semantic relations in a structured manner.

In this paper we propose the use of knowledge graph methods to tackle record linkage tasks. The proposed method, named WERL, takes advantage of the main knowledge graph properties and learns embedding vectors to encode census information. These embeddings are properly weighted to maximize the record linkage performance. We have evaluated this method on benchmark data sets and we have compared it to related methods with stimulating and satisfactory results.
 
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  Notes DAG; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ GRP2020 Serial (down) 3453  
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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 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 (down) 3451  
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Author Mohamed Ali Souibgui; Y.Kessentini; Alicia Fornes edit   pdf
openurl 
  Title A conditional GAN based approach for distorted camera captured documents recovery Type Conference Article
  Year 2020 Publication 4th Mediterranean Conference on Pattern Recognition and Artificial Intelligence Abbreviated Journal  
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  Address Virtual; December 2020  
  Corporate Author Thesis  
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  Area Expedition Conference MedPRAI  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ SKF2020 Serial (down) 3450  
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Author Mohamed Ali Souibgui; Alicia Fornes; Y.Kessentini; C.Tudor edit   pdf
doi  openurl
  Title A Few-shot Learning Approach for Historical Encoded Manuscript Recognition Type Conference Article
  Year 2021 Publication 25th International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 5413-5420  
  Keywords  
  Abstract Encoded (or ciphered) manuscripts are a special type of historical documents that contain encrypted text. The automatic recognition of this kind of documents is challenging because: 1) the cipher alphabet changes from one document to another, 2) there is a lack of annotated corpus for training and 3) touching symbols make the symbol segmentation difficult and complex. To overcome these difficulties, we propose a novel method for handwritten ciphers recognition based on few-shot object detection. Our method first detects all symbols of a given alphabet in a line image, and then a decoding step maps the symbol similarity scores to the final sequence of transcribed symbols. By training on synthetic data, we show that the proposed architecture is able to recognize handwritten ciphers with unseen alphabets. In addition, if few labeled pages with the same alphabet are used for fine tuning, our method surpasses existing unsupervised and supervised HTR methods for ciphers recognition.  
  Address Virtual; January 2021  
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  ISSN ISBN Medium  
  Area Expedition Conference ICPR  
  Notes DAG; 600.121; 600.140 Approved no  
  Call Number Admin @ si @ SFK2021 Serial (down) 3449  
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