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Author |
Dimosthenis Karatzas; V. Poulain d'Andecy; Marçal Rusiñol |
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Human-Document Interaction – a new frontier for document image analysis |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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369-374 |
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All indications show that paper documents will not cede in favour of their digital counterparts, but will instead be used increasingly in conjunction with digital information. An open challenge is how to seamlessly link the physical with the digital – how to continue taking advantage of the important affordances of paper, without missing out on digital functionality. This paper
presents the authors’ experience with developing systems for Human-Document Interaction based on augmented document interfaces and examines new challenges and opportunities arising for the document image analysis field in this area. The system presented combines state of the art camera-based document
image analysis techniques with a range of complementary tech-nologies to offer fluid Human-Document Interaction. Both fixed and nomadic setups are discussed that have gone through user testing in real-life environments, and use cases are presented that span the spectrum from business to educational application |
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Santorini; Greece; April 2016 |
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DAG; 600.084; 600.077 |
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KPR2016 |
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2756 |
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Joan Mas; Jose Antonio Rodriguez; Dimosthenis Karatzas; Gemma Sanchez; Josep Llados |
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Title |
HistoSketch: A Semi-Automatic Annotation Tool for Archival Documents |
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2008 |
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Proceedings of the 8th International Workshop on Document Analysis Systems, |
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517–524 |
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Nara (Japan) |
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DAG @ dag @ MRK2008a |
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1061 |
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Author |
Lluis Gomez; Dena Bazazian; Dimosthenis Karatzas |
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Title |
Historical review of scene text detection research |
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2020 |
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Visual Text Interpretation – Algorithms and Applications in Scene Understanding and Document Analysis |
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Springer |
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K. Alahari; C.V. Jawahar |
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Series on Advances in Computer Vision and Pattern Recognition |
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DAG; 600.121 |
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Admin @ si @ GBK2020 |
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3495 |
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Beata Megyesi; Alicia Fornes; Nils Kopal; Benedek Lang |
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Title |
Historical Cryptology |
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2024 |
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Learning and Experiencing Cryptography with CrypTool and SageMath |
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Historical cryptology studies (original) encrypted manuscripts, often handwritten sources, produced in our history. These historical sources can be found in archives, often hidden without any indexing and therefore hard to locate. Once found they need to be digitized and turned into a machine-readable text format before they can be deciphered with computational methods. The focus of historical cryptology is not primarily the development of sophisticated algorithms for decipherment, but rather the entire process of analysis of the encrypted source from collection and digitization to transcription and decryption. The process also includes the interpretation and contextualization of the message set in its historical context. There are many challenges on the way, such as mistakes made by the scribe, errors made by the transcriber, damaged pages, handwriting styles that are difficult to interpret, historical languages from various time periods, and hidden underlying language of the message. Ciphertexts vary greatly in terms of their code system and symbol sets used with more or less distinguishable symbols. Ciphertexts can be embedded in clearly written text, or shorter or longer sequences of cleartext can be embedded in the ciphertext. The ciphers used mostly in historical times are substitutions (simple, homophonic, or polyphonic), with or without nomenclatures, encoded as digits or symbol sequences, with or without spaces. So the circumstances are different from those in modern cryptography which focuses on methods (algorithms) and their strengths and assumes that the algorithm is applied correctly. For both historical and modern cryptology, attack vectors outside the algorithm are applied like implementation flaws and side-channel attacks. In this chapter, we give an introduction to the field of historical cryptology and present an overview of how researchers today process historical encrypted sources. |
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Admin @ si @ MFK2024 |
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4020 |
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Salvatore Tabbone; Oriol Ramos Terrades; S. Barrat |
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Histogram of radon transform. A useful descriptor for shape retrieval |
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2008 |
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19th International Conference on Pattern Recognition |
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1-4 |
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Tampa, Florida |
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ICPR |
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Admin @ si @ TRB2008 |
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1876 |
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Author |
Agnes Borras; Francesc Tous; Josep Llados; Maria Vanrell |
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Title |
High-Level Clothes Description Based on Colour-Texture and Structural Features |
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2003 |
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1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 |
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Palma de Mallorca |
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DAG;CIC |
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CAT @ cat @ BTL2003b |
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369 |
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Agnes Borras; Francesc Tous; Josep Llados; Maria Vanrell |
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High-Level Clothes Description Based on Color-Texture and Structural Features |
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2003 |
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Lecture Notes in Computer Science |
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2652 |
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108–116 |
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This work is a part of a surveillance system where content- based image retrieval is done in terms of people appearance. Given an image of a person, our work provides an automatic description of his clothing according to the colour, texture and structural composition of its garments. We present a two-stage process composed by image segmentation and a region-based interpretation. We segment an image by modelling it due to an attributed graph and applying a hybrid method that follows a split-and-merge strategy. We propose the interpretation of five cloth combinations that are modelled in a graph structure in terms of region features. The interpretation is viewed as a graph matching with an associated cost between the segmentation and the cloth models. Fi- nally, we have tested the process with a ground-truth of one hundred images. |
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Springer-Verlag |
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DAG;CIC |
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CAT @ cat @ BTL2003a |
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368 |
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Author |
Anjan Dutta; Pau Riba; Josep Llados; Alicia Fornes |
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Title |
Hierarchical Stochastic Graphlet Embedding for Graph-based Pattern Recognition |
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2020 |
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Neural Computing and Applications |
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NEUCOMA |
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32 |
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11579–11596 |
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Despite being very successful within the pattern recognition and machine learning community, graph-based methods are often unusable because of the lack of mathematical operations defined in graph domain. Graph embedding, which maps graphs to a vectorial space, has been proposed as a way to tackle these difficulties enabling the use of standard machine learning techniques. However, it is well known that graph embedding functions usually suffer from the loss of structural information. In this paper, we consider the hierarchical structure of a graph as a way to mitigate this loss of information. The hierarchical structure is constructed by topologically clustering the graph nodes and considering each cluster as a node in the upper hierarchical level. Once this hierarchical structure is constructed, we consider several configurations to define the mapping into a vector space given a classical graph embedding, in particular, we propose to make use of the stochastic graphlet embedding (SGE). Broadly speaking, SGE produces a distribution of uniformly sampled low-to-high-order graphlets as a way to embed graphs into the vector space. In what follows, the coarse-to-fine structure of a graph hierarchy and the statistics fetched by the SGE complements each other and includes important structural information with varied contexts. Altogether, these two techniques substantially cope with the usual information loss involved in graph embedding techniques, obtaining a more robust graph representation. This fact has been corroborated through a detailed experimental evaluation on various benchmark graph datasets, where we outperform the state-of-the-art methods. |
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DAG; 600.140; 600.121; 600.141 |
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Admin @ si @ DRL2020 |
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3348 |
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Klaus Broelemann; Anjan Dutta; Xiaoyi Jiang; Josep Llados |
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Title |
Hierarchical Plausibility-Graphs for Symbol Spotting in Graphical Documents |
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2014 |
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Graphics Recognition. Current Trends and Challenges |
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8746 |
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25-37 |
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Graph representation of graphical documents often suffers from noise such as spurious nodes and edges, and their discontinuity. In general these errors occur during the low-level image processing viz. binarization, skeletonization, vectorization etc. Hierarchical graph representation is a nice and efficient way to solve this kind of problem by hierarchically merging node-node and node-edge depending on the distance. But the creation of hierarchical graph representing the graphical information often uses hard thresholds on the distance to create the hierarchical nodes (next state) of the lower nodes (or states) of a graph. As a result, the representation often loses useful information. This paper introduces plausibilities to the nodes of hierarchical graph as a function of distance and proposes a modified algorithm for matching subgraphs of the hierarchical graphs. The plausibility-annotated nodes help to improve the performance of the matching algorithm on two hierarchical structures. To show the potential of this approach, we conduct an experiment with the SESYD dataset. |
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Springer Berlin Heidelberg |
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Bart Lamiroy; Jean-Marc Ogier |
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0302-9743 |
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978-3-662-44853-3 |
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DAG; 600.045; 600.056; 600.061; 600.077 |
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Admin @ si @ BDJ2014 |
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2699 |
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Ruben Tito; Dimosthenis Karatzas; Ernest Valveny |
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Hierarchical multimodal transformers for Multipage DocVQA |
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2023 |
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Pattern Recognition |
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PR |
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144 |
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109834 |
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Existing work on DocVQA only considers single-page documents. However, in real applications documents are mostly composed of multiple pages that should be processed altogether. In this work, we propose a new multimodal hierarchical method Hi-VT5, that overcomes the limitations of current methods to process long multipage documents. In contrast to previous hierarchical methods that focus on different semantic granularity (He et al., 2021) or different subtasks (Zhou et al., 2022) used in image classification. Our method is a hierarchical transformer architecture where the encoder learns to summarize the most relevant information of every page and then, the decoder uses this summarized representation to generate the final answer, following a bottom-up approach. Moreover, due to the lack of multipage DocVQA datasets, we also introduce MP-DocVQA, an extension of SP-DocVQA where questions are posed over multipage documents instead of single pages. Through extensive experimentation, we demonstrate that Hi-VT5 is able, in a single stage, to answer the questions and provide the page that contains the answer, which can be used as a kind of explainability measure. |
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Admin @ si @ TKV2023 |
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