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Josep Llados, Enric Marti, & Jaime Lopez-Krahe. (1999). A Hough-based method for hatched pattern detection in maps and diagrams. In Proceeding of the Fifth Int. Conf. Document Analysis and Recognition ICDAR ’99 (pp. 479–482).
Abstract: A hatched area is characterized by a set of parallel straight lines placed at regular intervals. In this paper, a Hough-based schema is introduced to recognize hatched areas in technical documents from attributed graph structures representing the document once it has been vectorized. Defining a Hough-based transform from a graph instead of the raster image allows to drastically reduce the processing time and, second, to obtain more reliable results because straight lines have already been detected in the vectorization step. A second advantage of the proposed method is that no assumptions must be made a priori about the slope and frequency of hatching patterns, but they are computed in run time for each hatched area.
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J.M. Sanchez, X. Binefa, & J.R. Kender. (2002). Multiple Feature Temporal Models for Object Detection in Video..
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J.M. Sanchez, X. Binefa, & J.R. Kender. (2002). Coupled Markox Chains for Video Contents Characterization..
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V. Valev, & Petia Radeva. (1992). Determining structural descriptions by boolean formulas advances in structural and syntactic Pattern Recognition..
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Ernest Valveny, Ricardo Toledo, Ramon Baldrich, & Enric Marti. (2002). Combining recognition-based in segmentation-based approaches for graphic symol recognition using deformable template matching. In Proceeding of the Second IASTED International Conference Visualization, Imaging and Image Proceesing VIIP 2002 (502–507).
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Ramon Baldrich, Ricardo Toledo, Ernest Valveny, & Maria Vanrell. (2002). Perceptual Colour Image Segmentation..
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David Lloret, & Joan Serrat. (1999). System for calibration of a stereotatic frame..
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Carlo Gatta, Oriol Pujol, O. Rodriguez-Leor, J. Mauri, & Petia Radeva. (2008). Robust Image-based IVUS Pullbacks Gating. In Proceedings 11th International ConferenceMedical Image Computing and Computer–Assisted Intervention (Vol. 5242, 518–525). LNCS.
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David Guillamet, & Jordi Vitria. (2001). Unsupervised Learning of Part-Based Representations.
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Robert Benavente, & Maria Vanrell. (2007). Parametrizacion del Espacio de Categorias de Color.
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Emanuele Vivoli, Ali Furkan Biten, Andres Mafla, Dimosthenis Karatzas, & Lluis Gomez. (2022). MUST-VQA: MUltilingual Scene-text VQA. In Proceedings European Conference on Computer Vision Workshops (Vol. 13804, 345–358). LNCS.
Abstract: 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.
Keywords: Visual question answering; Scene text; Translation robustness; Multilingual models; Zero-shot transfer; Power of language models
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Sergi Garcia Bordils, Andres Mafla, Ali Furkan Biten, Oren Nuriel, Aviad Aberdam, Shai Mazor, et al. (2022). Out-of-Vocabulary Challenge Report. In Proceedings European Conference on Computer Vision Workshops (Vol. 13804, 359–375). LNCS.
Abstract: This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge. The OOV contest introduces an important aspect that is not commonly studied by Optical Character Recognition (OCR) models, namely, the recognition of unseen scene text instances at training time. The competition compiles a collection of public scene text datasets comprising of 326,385 images with 4,864,405 scene text instances, thus covering a wide range of data distributions. A new and independent validation and test set is formed with scene text instances that are out of vocabulary at training time. The competition was structured in two tasks, end-to-end and cropped scene text recognition respectively. A thorough analysis of results from baselines and different participants is presented. Interestingly, current state-of-the-art models show a significant performance gap under the newly studied setting. We conclude that the OOV dataset proposed in this challenge will be an essential area to be explored in order to develop scene text models that achieve more robust and generalized predictions.
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Petia Radeva, & Jordi Vitria. (2001). Region Based Approach for Discriminant Snakes..
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Josep Llados, & Gemma Sanchez. (2003). Symbol Recognition Using Graphs.
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Francisco Jose Perales, Juan J. Villanueva, & Y. Luo. (1991). An automatic two-camera human motion perception system based on biomechanical model matching..
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