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Pau Torras; Mohamed Ali Souibgui; Sanket Biswas; Alicia Fornes |
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Title |
Segmentation-Free Alignment of Arbitrary Symbol Transcripts to Images |
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2023 |
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Document Analysis and Recognition – ICDAR 2023 Workshops |
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14193 |
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83-93 |
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Historical Manuscripts; Symbol Alignment |
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Developing arbitrary symbol recognition systems is a challenging endeavour. Even using content-agnostic architectures such as few-shot models, performance can be substantially improved by providing a number of well-annotated examples into training. In some contexts, transcripts of the symbols are available without any position information associated to them, which enables using line-level recognition architectures. A way of providing this position information to detection-based architectures is finding systems that can align the input symbols with the transcription. In this paper we discuss some symbol alignment techniques that are suitable for low-data scenarios and provide an insight on their perceived strengths and weaknesses. In particular, we study the usage of Connectionist Temporal Classification models, Attention-Based Sequence to Sequence models and we compare them with the results obtained on a few-shot recognition system. |
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Admin @ si @ TSS2023 |
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3850 |
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Author |
Philippe Dosch; Ernest Valveny |
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Title |
Report on the Second Symbol Recognition Contest |
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2006 |
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Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 381–397 |
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DAG @ dag @ DoV2006 |
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691 |
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Philippe Dosch; Josep Llados |
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Title |
Vectorial Signatures for Symbol Discrimination |
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2003 |
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Proceedings of Fifth IAPR International Workshop on Graphics Recognition, 159–169 |
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Barcelona |
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DAG @ dag @ DoL2003 |
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373 |
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Philippe Dosch; Josep Llados |
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Vectorial Signatures for Symbol Discrimination |
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2004 |
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Graphics Recognition: Recent Advances and Perspectives, J. Llados, Y.B. Kwon (Eds.), Lecture Notes in Computer Science, 3088:150–161, ISBN: 3–540–22478–5 |
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Springer-Verlag |
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DAG @ dag @ DoL2004 |
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461 |
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Author |
Q. Bao; Marçal Rusiñol; M.Coustaty; Muhammad Muzzamil Luqman; C.D. Tran; Jean-Marc Ogier |
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Title |
Delaunay triangulation-based features for Camera-based document image retrieval system |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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1-6 |
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Camera-based Document Image Retrieval; Delaunay Triangulation; Feature descriptors; Indexing |
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In this paper, we propose a new feature vector, named DElaunay TRIangulation-based Features (DETRIF), for real-time camera-based document image retrieval. DETRIF is computed based on the geometrical constraints from each pair of adjacency triangles in delaunay triangulation which is constructed from centroids of connected components. Besides, we employ a hashing-based indexing system in order to evaluate the performance of DETRIF and to compare it with other systems such as LLAH and SRIF. The experimentation is carried out on two datasets comprising of 400 heterogeneous-content complex linguistic map images (huge size, 9800 X 11768 pixels resolution)and 700 textual document images. |
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Santorini; Greece; April 2016 |
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DAG; 600.061; 600.084; 600.077 |
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Admin @ si @ BRC2016 |
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2757 |
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R. Bertrand; Oriol Ramos Terrades; P. Gomez-Kramer; P. Franco; Jean-Marc Ogier |
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Title |
A Conditional Random Field model for font forgery detection |
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Conference Article |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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576 - 580 |
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Nowadays, document forgery is becoming a real issue. A large amount of documents that contain critical information as payment slips, invoices or contracts, are constantly subject to fraudster manipulation because of the lack of security regarding this kind of document. Previously, a system to detect fraudulent documents based on its intrinsic features has been presented. It was especially designed to retrieve copy-move forgery and imperfection due to fraudster manipulation. However, when a set of characters is not present in the original document, copy-move forgery is not feasible. Hence, the fraudster will use a text toolbox to add or modify information in the document by imitating the font or he will cut and paste characters from another document where the font properties are similar. This often results in font type errors. Thus, a clue to detect document forgery consists of finding characters, words or sentences in a document with font properties different from their surroundings. To this end, we present in this paper an automatic forgery detection method based on document font features. Using the Conditional Random Field a measurement of probability that a character belongs to a specific font is made by comparing the character font features to a knowledge database. Then, the character is classified as a genuine or a fake one by comparing its probability to belong to a certain font type with those of the neighboring characters. |
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Nancy; France; August 2015 |
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DAG; 600.077 |
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Admin @ si @ BRG2015 |
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2725 |
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R. Bertrand; P. Gomez-Krämer; Oriol Ramos Terrades; P. Franco; Jean-Marc Ogier |
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A System Based On Intrinsic Features for Fraudulent Document Detection |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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106-110 |
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paper document; document analysis; fraudulent document; forgery; fake |
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Paper documents still represent a large amount of information supports used nowadays and may contain critical data. Even though official documents are secured with techniques such as printed patterns or artwork, paper documents suffer froma lack of security.
However, the high availability of cheap scanning and printing hardware allows non-experts to easily create fake documents. As the use of a watermarking system added during the document production step is hardly possible, solutions have to be proposed to distinguish a genuine document from a forged one.
In this paper, we present an automatic forgery detection method based on document’s intrinsic features at character level. This method is based on the one hand on outlier character detection in a discriminant feature space and on the other hand on the detection of strictly similar characters. Therefore, a feature set iscomputed for all characters. Then, based on a distance between characters of the same class. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG; 600.061 |
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Admin @ si @ BGR2013a |
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2332 |
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Author |
Rahat Khan; Joost Van de Weijer; Dimosthenis Karatzas; Damien Muselet |
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Towards multispectral data acquisition with hand-held devices |
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2013 |
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20th IEEE International Conference on Image Processing |
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2053 - 2057 |
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Multispectral; mobile devices; color measurements |
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We propose a method to acquire multispectral data with handheld devices with front-mounted RGB cameras. We propose to use the display of the device as an illuminant while the camera captures images illuminated by the red, green and
blue primaries of the display. Three illuminants and three response functions of the camera lead to nine response values which are used for reflectance estimation. Results are promising and show that the accuracy of the spectral reconstruction improves in the range from 30-40% over the spectral
reconstruction based on a single illuminant. Furthermore, we propose to compute sensor-illuminant aware linear basis by discarding the part of the reflectances that falls in the sensorilluminant null-space. We show experimentally that optimizing reflectance estimation on these new basis functions decreases
the RMSE significantly over basis functions that are independent to sensor-illuminant. We conclude that, multispectral data acquisition is potentially possible with consumer hand-held devices such as tablets, mobiles, and laptops, opening up applications which are currently considered to be unrealistic. |
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Melbourne; Australia; September 2013 |
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ICIP |
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CIC; DAG; 600.048 |
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Admin @ si @ KWK2013b |
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2265 |
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Ramon Baldrich; Ricardo Toledo; Ernest Valveny; Maria Vanrell |
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Perceptual Colour Image Segmentation. |
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2002 |
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Proceeding of the Second IASTED International Conference Visualization, Imaging and Image Proceesing VIIP 2002: 145–150. |
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DAG;CIC;ADAS |
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CAT @ cat @ BTV2002 |
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290 |
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Raul Gomez |
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Exploiting the Interplay between Visual and Textual Data for Scene Interpretation |
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2020 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Machine learning experimentation under controlled scenarios and standard datasets is necessary to compare algorithms performance by evaluating all of them in the same setup. However, experimentation on how those algorithms perform on unconstrained data and applied tasks to solve real world problems is also a must to ascertain how that research can contribute to our society.
In this dissertation we experiment with the latest computer vision and natural language processing algorithms applying them to multimodal scene interpretation. Particularly, we research on how image and text understanding can be jointly exploited to address real world problems, focusing on learning from Social Media data.
We address several tasks that involve image and textual information, discuss their characteristics and offer our experimentation conclusions. First, we work on detection of scene text in images. Then, we work with Social Media posts, exploiting the captions associated to images as supervision to learn visual features, which we apply to multimodal semantic image retrieval. Subsequently, we work with geolocated Social Media images with associated tags, experimenting on how to use the tags as supervision, on location sensitive image retrieval and on exploiting location information for image tagging. Finally, we work on a specific classification problem of Social Media publications consisting on an image and a text: Multimodal hate speech classification. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Dimosthenis Karatzas;Lluis Gomez;Jaume Gibert |
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978-84-121011-7-1 |
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DAG; 600.121 |
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Admin @ si @ Gom20 |
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3479 |
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