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Author Alicia Fornes; Josep Llados; Gemma Sanchez edit  openurl
  Title (up) Primitive Segmentation in Old Handwritten Music Scores Type Book Chapter
  Year 2006 Publication Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 288–299 Abbreviated Journal  
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  Notes DAG Approved no  
  Call Number DAG @ dag @ FLS2006 Serial 697  
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Author Alfons Juan-Ciscar; Gemma Sanchez edit  openurl
  Title (up) PRIS 2008. Pattern Recognition in Information Systems. Proceedings of the 8th international Workshop on Pattern Recognition in Information systems – PRIS 2008, in conjunction with ICEIS 2008 Type Book Whole
  Year 2008 Publication Abbreviated Journal  
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  Address Barcelona (Spain)  
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  Notes DAG Approved no  
  Call Number DAG @ dag @ JuS2008 Serial 1054  
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Author Ruben Perez Tito; Khanh Nguyen; Marlon Tobaben; Raouf Kerkouche; Mohamed Ali Souibgui; Kangsoo Jung; Lei Kang; Ernest Valveny; Antti Honkela; Mario Fritz; Dimosthenis Karatzas edit   pdf
url  openurl
  Title (up) Privacy-Aware Document Visual Question Answering Type Miscellaneous
  Year 2023 Publication Arxiv Abbreviated Journal  
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  Abstract Document Visual Question Answering (DocVQA) is a fast growing branch of document understanding. Despite the fact that documents contain sensitive or copyrighted information, none of the current DocVQA methods offers strong privacy guarantees.
In this work, we explore privacy in the domain of DocVQA for the first time. We highlight privacy issues in state of the art multi-modal LLM models used for DocVQA, and explore possible solutions.
Specifically, we focus on the invoice processing use case as a realistic, widely used scenario for document understanding, and propose a large scale DocVQA dataset comprising invoice documents and associated questions and answers. We employ a federated learning scheme, that reflects the real-life distribution of documents in different businesses, and we explore the use case where the ID of the invoice issuer is the sensitive information to be protected.
We demonstrate that non-private models tend to memorise, behaviour that can lead to exposing private information. We then evaluate baseline training schemes employing federated learning and differential privacy in this multi-modal scenario, where the sensitive information might be exposed through any of the two input modalities: vision (document image) or language (OCR tokens).
Finally, we design an attack exploiting the memorisation effect of the model, and demonstrate its effectiveness in probing different DocVQA models.
 
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  Notes DAG Approved no  
  Call Number Admin @ si @ PNT2023 Serial 4012  
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Author Francisco Cruz edit  isbn
openurl 
  Title (up) Probabilistic Graphical Models for Document Analysis Type Book Whole
  Year 2016 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
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  Abstract Latest advances in digitization techniques have fostered the interest in creating digital copies of collections of documents. Digitized documents permit an easy maintenance, loss-less storage, and efficient ways for transmission and to perform information retrieval processes. This situation has opened a new market niche to develop systems able to automatically extract and analyze information contained in these collections, specially in the ambit of the business activity.

Due to the great variety of types of documents this is not a trivial task. For instance, the automatic extraction of numerical data from invoices differs substantially from a task of text recognition in historical documents. However, in order to extract the information of interest, is always necessary to identify the area of the document where it is located. In the area of Document Analysis we refer to this process as layout analysis, which aims at identifying and categorizing the different entities that compose the document, such as text regions, pictures, text lines, or tables, among others. To perform this task it is usually necessary to incorporate a prior knowledge about the task into the analysis process, which can be modeled by defining a set of contextual relations between the different entities of the document. The use of context has proven to be useful to reinforce the recognition process and improve the results on many computer vision tasks. It presents two fundamental questions: What kind of contextual information is appropriate for a given task, and how to incorporate this information into the models.

In this thesis we study several ways to incorporate contextual information to the task of document layout analysis, and to the particular case of handwritten text line segmentation. We focus on the study of Probabilistic Graphical Models and other mechanisms for this purpose, and propose several solutions to these problems. First, we present a method for layout analysis based on Conditional Random Fields. With this model we encode local contextual relations between variables, such as pair-wise constraints. Besides, we encode a set of structural relations between different classes of regions at feature level. Second, we present a method based on 2D-Probabilistic Context-free Grammars to encode structural and hierarchical relations. We perform a comparative study between Probabilistic Graphical Models and this syntactic approach. Third, we propose a method for structured documents based on Bayesian Networks to represent the document structure, and an algorithm based in the Expectation-Maximization to find the best configuration of the page. We perform a thorough evaluation of the proposed methods on two particular collections of documents: a historical collection composed of ancient structured documents, and a collection of contemporary documents. In addition, we present a general method for the task of handwritten text line segmentation. We define a probabilistic framework where we combine the EM algorithm with variational approaches for computing inference and parameter learning on a Markov Random Field. We evaluate our method on several collections of documents, including a general dataset of annotated administrative documents. Results demonstrate the applicability of our method to real problems, and the contribution of the use of contextual information to this kind of problems.
 
  Address  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor Oriol Ramos Terrades  
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  ISSN ISBN 978-84-945373-2-5 Medium  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number Admin @ si @ Cru2016 Serial 2861  
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Author Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal edit   pdf
url  openurl
  Title (up) Product graph-based higher order contextual similarities for inexact subgraph matching Type Journal Article
  Year 2018 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 76 Issue Pages 596-611  
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  Abstract Many algorithms formulate graph matching as an optimization of an objective function of pairwise quantification of nodes and edges of two graphs to be matched. Pairwise measurements usually consider local attributes but disregard contextual information involved in graph structures. We address this issue by proposing contextual similarities between pairs of nodes. This is done by considering the tensor product graph (TPG) of two graphs to be matched, where each node is an ordered pair of nodes of the operand graphs. Contextual similarities between a pair of nodes are computed by accumulating weighted walks (normalized pairwise similarities) terminating at the corresponding paired node in TPG. Once the contextual similarities are obtained, we formulate subgraph matching as a node and edge selection problem in TPG. We use contextual similarities to construct an objective function and optimize it with a linear programming approach. Since random walk formulation through TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities and better discrimination among the nodes and edges. Experimental results shown on synthetic as well as real benchmarks illustrate that higher order contextual similarities increase discriminating power and allow one to find approximate solutions to the subgraph matching problem.  
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  Notes DAG; 602.167; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ DLB2018 Serial 3083  
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Author T.O. Nguyen; Salvatore Tabbone; Oriol Ramos Terrades; A.T. Thierry edit  openurl
  Title (up) Proposition d'un descripteur de formes et du modèle vectoriel pour la recherche de symboles Type Conference Article
  Year 2008 Publication Colloque International Francophone sur l'Ecrit et le Document Abbreviated Journal  
  Volume Issue Pages 79-84  
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  Address Rouen, France  
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  Area Expedition Conference CIFED  
  Notes DAG Approved no  
  Call Number Admin @ si @ NTR2008b Serial 1875  
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Author Fernando Vilariño edit  openurl
  Title (up) 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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  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 3458  
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Author Anjan Dutta; Pau Riba; Josep Llados; Alicia Fornes edit   pdf
openurl 
  Title (up) Pyramidal Stochastic Graphlet Embedding for Document Pattern Classification Type Conference Article
  Year 2017 Publication 14th International Conference on Document Analysis and Recognition Abbreviated Journal  
  Volume Issue Pages 33-38  
  Keywords graph embedding; hierarchical graph representation; graph clustering; stochastic graphlet embedding; graph classification  
  Abstract Document pattern classification methods using graphs have received a lot of attention because of its robust representation paradigm and rich theoretical background. However, the way of preserving and the process for delineating documents with graphs introduce noise in the rendition of underlying data, which creates instability in the graph representation. To deal with such unreliability in representation, in this paper, we propose Pyramidal Stochastic Graphlet Embedding (PSGE).
Given a graph representing a document pattern, our method first computes a graph pyramid by successively reducing the base graph. Once the graph pyramid is computed, we apply Stochastic Graphlet Embedding (SGE) for each level of the pyramid and combine their embedded representation to obtain a global delineation of the original graph. The consideration of pyramid of graphs rather than just a base graph extends the representational power of the graph embedding, which reduces the instability caused due to noise and distortion. When plugged with support
vector machine, our proposed PSGE has outperformed the state-of-the-art results in recognition of handwritten words as well as graphical symbols
 
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  Notes DAG; 600.097; 601.302; 600.121 Approved no  
  Call Number Admin @ si @ DRL2017 Serial 3054  
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Author Suman Ghosh; Ernest Valveny edit   pdf
url  doi
openurl 
  Title (up) Query by String word spotting based on character bi-gram indexing Type Conference Article
  Year 2015 Publication 13th International Conference on Document Analysis and Recognition ICDAR2015 Abbreviated Journal  
  Volume Issue Pages 881-885  
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  Abstract In this paper we propose a segmentation-free query by string word spotting method. Both the documents and query strings are encoded using a recently proposed word representa- tion that projects images and strings into a common atribute space based on a pyramidal histogram of characters(PHOC). These attribute models are learned using linear SVMs over the Fisher Vector representation of the images along with the PHOC labels of the corresponding strings. In order to search through the whole page, document regions are indexed per character bi- gram using a similar attribute representation. On top of that, we propose an integral image representation of the document using a simplified version of the attribute model for efficient computation. Finally we introduce a re-ranking step in order to boost retrieval performance. We show state-of-the-art results for segmentation-free query by string word spotting in single-writer and multi-writer standard datasets  
  Address Nancy; France; August 2015  
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  Notes DAG; 600.077 Approved no  
  Call Number Admin @ si @ GhV2015a Serial 2715  
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Author Partha Pratim Roy; Umapada Pal; Josep Llados edit  doi
isbn  openurl
  Title (up) Query Driven Word Retrieval in Graphical Documents Type Conference Article
  Year 2010 Publication 9th IAPR International Workshop on Document Analysis Systems Abbreviated Journal  
  Volume Issue Pages 191–198  
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  Abstract In this paper, we present an approach towards the retrieval of words from graphical document images. In graphical documents, due to presence of multi-oriented characters in non-structured layout, word indexing is a challenging task. The proposed approach uses recognition results of individual components to form character pairs with the neighboring components. An indexing scheme is designed to store the spatial description of components and to access them efficiently. Given a query text word (ascii/unicode format), the character pairs present in it are searched in the document. Next the retrieved character pairs are linked sequentially to form character string. Dynamic programming is applied to find different instances of query words. A string edit distance is used here to match the query word as the objective function. Recognition of multi-scale and multi-oriented character component is done using Support Vector Machine classifier. To consider multi-oriented character strings the features used in the SVM are invariant to character orientation. Experimental results show that the method is efficient to locate a query word from multi-oriented text in graphical documents.  
  Address Boston; USA  
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  ISSN ISBN 978-1-60558-773-8 Medium  
  Area Expedition Conference DAS  
  Notes DAG Approved no  
  Call Number DAG @ dag @ RPL2010b Serial 1433  
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