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Thanh Ha Do, Salvatore Tabbone and Oriol Ramos Terrades. 2012. Noise suppression over bi-level graphical documents using a sparse representation. Colloque International Francophone sur l'Écrit et le Document.
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Christophe Rigaud and Clement Guerin. 2014. Localisation contextuelle des personnages de bandes dessinées. Colloque International Francophone sur l'Écrit et le Document.
Abstract: Les auteurs proposent une méthode de localisation des personnages dans des cases de bandes dessinées en s'appuyant sur les caractéristiques des bulles de dialogue. L'évaluation montre un taux de localisation des personnages allant jusqu'à 65%.
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Marçal Rusiñol, J. Chazalon and Jean-Marc Ogier. 2014. Normalisation et validation d'images de documents capturées en mobilité. Colloque International Francophone sur l'Écrit et le Document.109–124.
Abstract: Mobile document image acquisition integrates many distortions which must be corrected or detected on the device, before the document becomes unavailable or paying data transmission fees. In this paper, we propose a system to correct perspective and illumination issues, and estimate the sharpness of the image for OCR recognition. The correction step relies on fast and accurate border detection followed by illumination normalization. Its evaluation on a private dataset shows a clear improvement on OCR accuracy. The quality assessment
step relies on a combination of focus measures. Its evaluation on a public dataset shows that this simple method compares well to state of the art, learning-based methods which cannot be embedded on a mobile, and outperforms metric-based methods.
Keywords: mobile document image acquisition; perspective correction; illumination correction; quality assessment; focus measure; OCR accuracy prediction
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P. Wang, V. Eglin, C. Garcia, C. Largeron, Josep Llados and Alicia Fornes. 2014. Représentation par graphe de mots manuscrits dans les images pour la recherche par similarité. Colloque International Francophone sur l'Écrit et le Document.233–248.
Abstract: Effective information retrieval on handwritten document images has always been
a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of handwriting. Skeleton-based graphs with the Shape Context labeled vertexes are established for connected components. Each word image is represented as a sequence of graphs. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment results introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods.
Keywords: word spotting; graph-based representation; shape context description; graph edit distance; DTW; block merging; query by example
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Marçal Rusiñol, J. Chazalon and Jean-Marc Ogier. 2016. Filtrage de descripteurs locaux pour l'amélioration de la détection de documents. Colloque International Francophone sur l'Écrit et le Document.
Abstract: In this paper we propose an effective method aimed at reducing the amount of local descriptors to be indexed in a document matching framework.In an off-line training stage, the matching between the model document and incoming images is computed retaining the local descriptors from the model that steadily produce good matches. We have evaluated this approach by using the ICDAR2015 SmartDOC dataset containing near 25000 images from documents to be captured by a mobile device. We have tested the performance of this filtering step by using ORB and SIFT local detectors and descriptors. The results show an important gain both in quality of the final matching as well as in time and space requirements.
Keywords: Local descriptors; mobile capture; document matching; keypoint selection
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Ernest Valveny and Miquel Ferrer. 2008. Application of Graph Embedding to Solve Graph Matchin Problems. Colloque International Francophone sur l’Ecrit et le Document.13–18.
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Marçal Rusiñol and 7 others. 2012. CVC-UAB's participation in the Flowchart Recognition Task of CLEF-IP 2012. Conference and Labs of the Evaluation Forum.
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Stepan Simsa and 10 others. 2023. Overview of DocILE 2023: Document Information Localization and Extraction. International Conference of the Cross-Language Evaluation Forum for European Languages.276–293. (LNCS.)
Abstract: This paper provides an overview of the DocILE 2023 Competition, its tasks, participant submissions, the competition results and possible future research directions. This first edition of the competition focused on two Information Extraction tasks, Key Information Localization and Extraction (KILE) and Line Item Recognition (LIR). Both of these tasks require detection of pre-defined categories of information in business documents. The second task additionally requires correctly grouping the information into tuples, capturing the structure laid out in the document. The competition used the recently published DocILE dataset and benchmark that stays open to new submissions. The diversity of the participant solutions indicates the potential of the dataset as the submissions included pure Computer Vision, pure Natural Language Processing, as well as multi-modal solutions and utilized all of the parts of the dataset, including the annotated, synthetic and unlabeled subsets.
Keywords: Information Extraction; Computer Vision; Natural Language Processing; Optical Character Recognition; Document Understanding
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Ali Furkan Biten and 8 others. 2019. ICDAR 2019 Competition on Scene Text Visual Question Answering. 3rd Workshop on Closing the Loop Between Vision and Language, in conjunction with ICCV2019.
Abstract: This paper presents final results of ICDAR 2019 Scene Text Visual Question Answering competition (ST-VQA). ST-VQA introduces an important aspect that is not addressed
by any Visual Question Answering system up to date, namely the incorporation of scene text to answer questions asked about an image. The competition introduces a new dataset comprising 23, 038 images annotated with 31, 791 question / answer pairs where the answer is always grounded on text instances present in the image. The images are taken from 7 different public computer vision datasets, covering a wide range of scenarios.
The competition was structured in three tasks of increasing difficulty, that require reading the text in a scene and understanding it in the context of the scene, to correctly answer a given question. A novel evaluation metric is presented, which elegantly assesses both key capabilities expected from an optimal model: text recognition and image understanding. A detailed analysis of results from different participants is showcased, which provides insight into the current capabilities of VQA systems that can read. We firmly believe the dataset proposed in this challenge will be an important milestone to consider towards a path of more robust and general models that
can exploit scene text to achieve holistic image understanding.
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Ariel Amato, Angel Sappa, Alicia Fornes, Felipe Lumbreras and Josep Llados. 2013. Divide and Conquer: Atomizing and Parallelizing A Task in A Mobile Crowdsourcing Platform. 2nd International ACM Workshop on Crowdsourcing for Multimedia.21–22.
Abstract: In this paper we present some conclusions about the advantages of having an efficient task formulation when a crowdsourcing platform is used. In particular we show how the task atomization and distribution can help to obtain results in an efficient way. Our proposal is based on a recursive splitting of the original task into a set of smaller and simpler tasks. As a result both more accurate and faster solutions are obtained. Our evaluation is performed on a set of ancient documents that need to be digitized.
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