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Pau Riba; Anjan Dutta; Lutz Goldmann; Alicia Fornes; Oriol Ramos Terrades; Josep Llados |
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Title |
Table Detection in Invoice Documents by Graph Neural Networks |
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2019 |
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15th International Conference on Document Analysis and Recognition |
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122-127 |
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Tabular structures in documents offer a complementary dimension to the raw textual data, representing logical or quantitative relationships among pieces of information. In digital mail room applications, where a large amount of
administrative documents must be processed with reasonable accuracy, the detection and interpretation of tables is crucial. Table recognition has gained interest in document image analysis, in particular in unconstrained formats (absence of rule lines, unknown information of rows and columns). In this work, we propose a graph-based approach for detecting tables in document images. Instead of using the raw content (recognized text), we make use of the location, context and content type, thus it is purely a structure perception approach, not dependent on the language and the quality of the text
reading. Our framework makes use of Graph Neural Networks (GNNs) in order to describe the local repetitive structural information of tables in invoice documents. Our proposed model has been experimentally validated in two invoice datasets and achieved encouraging results. Additionally, due to the scarcity
of benchmark datasets for this task, we have contributed to the community a novel dataset derived from the RVL-CDIP invoice data. It will be publicly released to facilitate future research. |
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Sydney; Australia; September 2019 |
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DAG; 600.140; 601.302; 602.167; 600.121; 600.141 |
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Admin @ si @ RDG2019 |
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3355 |
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Author |
Ekta Vats; Anders Hast; Alicia Fornes |
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Title |
Training-Free and Segmentation-Free Word Spotting using Feature Matching and Query Expansion |
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Conference Article |
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2019 |
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15th International Conference on Document Analysis and Recognition |
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1294-1299 |
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Word spotting; Segmentation-free; Trainingfree; Query expansion; Feature matching |
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Historical handwritten text recognition is an interesting yet challenging problem. In recent times, deep learning based methods have achieved significant performance in handwritten text recognition. However, handwriting recognition using deep learning needs training data, and often, text must be previously segmented into lines (or even words). These limitations constrain the application of HTR techniques in document collections, because training data or segmented words are not always available. Therefore, this paper proposes a training-free and segmentation-free word spotting approach that can be applied in unconstrained scenarios. The proposed word spotting framework is based on document query word expansion and relaxed feature matching algorithm, which can easily be parallelised. Since handwritten words posses distinct shape and characteristics, this work uses a combination of different keypoint detectors
and Fourier-based descriptors to obtain a sufficient degree of relaxed matching. The effectiveness of the proposed method is empirically evaluated on well-known benchmark datasets using standard evaluation measures. The use of informative features along with query expansion significantly contributed in efficient performance of the proposed method. |
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Sydney; Australia; September 2019 |
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DAG; 600.140; 600.121 |
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Admin @ si @ VHF2019 |
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3356 |
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Partha Pratim Roy; Umapada Pal; Josep Llados; F. Kimura |
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Convex Hull based Approach for Multi-oriented Character Recognition form Graphical Documents |
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2008 |
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19th International Conference on Pattern Recognition |
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Tampa (Florida) |
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DAG @ dag @ RPL2008d |
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1073 |
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H. Chouaib; Oriol Ramos Terrades; Salvatore Tabbone; F. Cloppet; N. Vincent |
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Feature Selection Combining Genetic Algorithm and Adaboost Classifiers |
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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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Admin @ si @ CRT2008 |
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1872 |
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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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DAG |
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Admin @ si @ TRB2008 |
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1876 |
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Author |
Miquel Ferrer; Ernest Valveny; F. Serratosa; K. Riesen; Horst Bunke |
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Title |
An Approximate Algorith for Median Graph Computation using Graph Embedding |
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2008 |
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19th International Conference on Pattern Recognition. |
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Tampa, USA |
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DAG @ dag @ FVS2008a |
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1064 |
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Dimosthenis Karatzas; Marçal Rusiñol; Coen Antens; Miquel Ferrer |
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Title |
Segmentation Robust to the Vignette Effect for Machine Vision Systems |
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2008 |
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19th International Conference on Pattern Recognition |
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The vignette effect (radial fall-off) is commonly encountered in images obtained through certain image acquisition setups and can seriously hinder automatic analysis processes. In this paper we present a fast and efficient method for dealing with vignetting in the context of object segmentation in an existing industrial inspection setup. The vignette effect is modelled here as a circular, non-linear gradient. The method estimates the gradient parameters and employs them to perform segmentation. Segmentation results on a variety of images indicate that the presented method is able to successfully tackle the vignette effect. |
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Tampa, USA |
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DAG |
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DAG @ dag @ KRA2008 |
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1065 |
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Jose Antonio Rodriguez; Florent Perronnin; Gemma Sanchez; Josep Llados |
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Title |
Unsupervised writer style adaptation for handwritten word spotting |
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2008 |
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Pattern Recognition. 19th International Conference on, IBM Best Student Paper Award. |
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Tampa, USA |
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DAG @ dag @ RPS2008 |
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1077 |
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Author |
Carles Sanchez; Oriol Ramos Terrades; Patricia Marquez; Enric Marti; Jaume Rocarias; Debora Gil |
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Title |
Evaluación automática de prácticas en Moodle para el aprendizaje autónomo en Ingenierías |
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Miscellaneous |
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2014 |
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8th International Congress on University Teaching and Innovation |
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Tarragona; juliol 2014 |
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CIDUI |
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IAM; 600.075;DAG |
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Admin @ si @ SRM2014 |
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2458 |
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Emanuele Vivoli; Ali Furkan Biten; Andres Mafla; Dimosthenis Karatzas; Lluis Gomez |
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Title |
MUST-VQA: MUltilingual Scene-text VQA |
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2022 |
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Proceedings European Conference on Computer Vision Workshops |
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13804 |
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345–358 |
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Visual question answering; Scene text; Translation robustness; Multilingual models; Zero-shot transfer; Power of language models |
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In this paper, we present a framework for Multilingual Scene Text Visual Question Answering that deals with new languages in a zero-shot fashion. Specifically, we consider the task of Scene Text Visual Question Answering (STVQA) in which the question can be asked in different languages and it is not necessarily aligned to the scene text language. Thus, we first introduce a natural step towards a more generalized version of STVQA: MUST-VQA. Accounting for this, we discuss two evaluation scenarios in the constrained setting, namely IID and zero-shot and we demonstrate that the models can perform on a par on a zero-shot setting. We further provide extensive experimentation and show the effectiveness of adapting multilingual language models into STVQA tasks. |
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Tel-Aviv; Israel; October 2022 |
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ECCVW |
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DAG; 302.105; 600.155; 611.002 |
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Admin @ si @ VBM2022 |
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3770 |
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