| Records |
| Author |
Dimosthenis Karatzas; Lluis Gomez; A.Nicolaou ; Suman Ghosh; Andrew Bagdanov; Masakazu Iwamura; J.Matas; L.Neumann; V.Ramaseshan; S.Lu ; Faisal Shafait; Seiichi Uchida; Ernest Valveny |
| Title |
ICDAR 2015 Competition on Robust Reading |
Type |
Conference Article |
| Year |
2015 |
Publication  |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
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| Volume |
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Issue |
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Pages |
1156-1160 |
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Conference |
ICDAR |
| Notes |
DAG; 600.077; 600.084 |
Approved |
no |
| Call Number |
Admin @ si @ KGN2015 |
Serial |
2690 |
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| Author |
Lluis Gomez; Dimosthenis Karatzas |
| Title |
Object Proposals for Text Extraction in the Wild |
Type |
Conference Article |
| Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
206 - 210 |
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| Abstract |
Object Proposals is a recent computer vision technique receiving increasing interest from the research community. Its main objective is to generate a relatively small set of bounding box proposals that are most likely to contain objects of interest. The use of Object Proposals techniques in the scene text understanding field is innovative. Motivated by the success of powerful while expensive techniques to recognize words in a holistic way, Object Proposals techniques emerge as an alternative to the traditional text detectors. In this paper we study to what extent the existing generic Object Proposals methods may be useful for scene text understanding. Also, we propose a new Object Proposals algorithm that is specifically designed for text and compare it with other generic methods in the state of the art. Experiments show that our proposal is superior in its ability of producing good quality word proposals in an efficient way. The source code of our method is made publicly available |
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ICDAR |
| Notes |
DAG; 600.077; 600.084; 601.197 |
Approved |
no |
| Call Number |
Admin @ si @ GoK2015 |
Serial |
2691 |
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| Author |
A.Nicolaou; Andrew Bagdanov; Marcus Liwicki; Dimosthenis Karatzas |
| Title |
Sparse Radial Sampling LBP for Writer Identification |
Type |
Conference Article |
| Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
716-720 |
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In this paper we present the use of Sparse Radial Sampling Local Binary Patterns, a variant of Local Binary Patterns (LBP) for text-as-texture classification. By adapting and extending the standard LBP operator to the particularities of text we get a generic text-as-texture classification scheme and apply it to writer identification. In experiments on CVL and ICDAR 2013 datasets, the proposed feature-set demonstrates State-Of-the-Art (SOA) performance. Among the SOA, the proposed method is the only one that is based on dense extraction of a single local feature descriptor. This makes it fast and applicable at the earliest stages in a DIA pipeline without the need for segmentation, binarization, or extraction of multiple features. |
| Address |
Nancy; France; August 2015 |
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ICDAR |
| Notes |
DAG; 600.077 |
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no |
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Admin @ si @ NBL2015 |
Serial |
2692 |
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| Author |
Suman Ghosh; Ernest Valveny |
| Title |
Query by String word spotting based on character bi-gram indexing |
Type |
Conference Article |
| Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
Abbreviated Journal |
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Issue |
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Pages |
881-885 |
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In this paper we propose a segmentation-free query by string word spotting method. Both the documents and query strings are encoded using a recently proposed word representa- tion that projects images and strings into a common atribute space based on a pyramidal histogram of characters(PHOC). These attribute models are learned using linear SVMs over the Fisher Vector representation of the images along with the PHOC labels of the corresponding strings. In order to search through the whole page, document regions are indexed per character bi- gram using a similar attribute representation. On top of that, we propose an integral image representation of the document using a simplified version of the attribute model for efficient computation. Finally we introduce a re-ranking step in order to boost retrieval performance. We show state-of-the-art results for segmentation-free query by string word spotting in single-writer and multi-writer standard datasets |
| Address |
Nancy; France; August 2015 |
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ICDAR |
| Notes |
DAG; 600.077 |
Approved |
no |
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Admin @ si @ GhV2015a |
Serial |
2715 |
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| Author |
R. Bertrand; Oriol Ramos Terrades; P. Gomez-Kramer; P. Franco; Jean-Marc Ogier |
| Title |
A Conditional Random Field model for font forgery detection |
Type |
Conference Article |
| Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
Abbreviated Journal |
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Issue |
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Pages |
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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ICDAR |
| Notes |
DAG; 600.077 |
Approved |
no |
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Admin @ si @ BRG2015 |
Serial |
2725 |
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| Author |
Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados; David Fernandez; Cristina Cañero |
| Title |
Use case visual Bag-of-Words techniques for camera based identity document classification |
Type |
Conference Article |
| Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
721 - 725 |
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| Abstract |
Nowadays, automatic identity document recognition, including passport and driving license recognition, is at the core of many applications within the administrative and service sectors, such as police, hospitality, car renting, etc. In former years, the document information was manually extracted whereas today this data is recognized automatically from images obtained by flat-bed scanners. Yet, since these scanners tend to be expensive and voluminous, companies in the sector have recently turned their attention to cheaper, small and yet computationally powerful scanners: the mobile devices. The document identity recognition from mobile images enclose several new difficulties w.r.t traditional scanned images, such as the loss of a controlled background, perspective, blurring, etc. In this paper we present a real application for identity document classification of images taken from mobile devices. This classification process is of extreme importance since a prior knowledge of the document type and origin strongly facilitates the subsequent information extraction. The proposed method is based on a traditional Bagof-Words in which we have taken into consideration several key aspects to enhance recognition rate. The method performance has been studied on three datasets containing more than 2000 images from 129 different document classes. |
| Address |
Nancy; France; August 2015 |
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ICDAR |
| Notes |
DAG; 600.077; 600.061; |
Approved |
no |
| Call Number |
Admin @ si @ HRL2015a |
Serial |
2726 |
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| Author |
Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados |
| Title |
Attributed Graph Grammar for floor plan analysis |
Type |
Conference Article |
| Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
726 - 730 |
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| Abstract |
In this paper, we propose the use of an Attributed Graph Grammar as unique framework to model and recognize the structure of floor plans. This grammar represents a building as a hierarchical composition of structurally and semantically related elements, where common representations are learned stochastically from annotated data. Given an input image, the parsing consists on constructing that graph representation that better agrees with the probabilistic model defined by the grammar. The proposed method provides several advantages with respect to the traditional floor plan analysis techniques. It uses an unsupervised statistical approach for detecting walls that adapts to different graphical notations and relaxes strong structural assumptions such are straightness and orthogonality. Moreover, the independence between the knowledge model and the parsing implementation allows the method to learn automatically different building configurations and thus, to cope the existing variability. These advantages are clearly demonstrated by comparing it with the most recent floor plan interpretation techniques on 4 datasets of real floor plans with different notations. |
| Address |
Nancy; France; August 2015 |
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ICDAR |
| Notes |
DAG; 600.077; 600.061 |
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no |
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Admin @ si @ HRL2015b |
Serial |
2727 |
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| Author |
Jon Almazan; David Fernandez; Alicia Fornes; Josep Llados; Ernest Valveny |
| Title |
A Coarse-to-Fine Approach for Handwritten Word Spotting in Large Scale Historical Documents Collection |
Type |
Conference Article |
| Year |
2012 |
Publication |
13th International Conference on Frontiers in Handwriting Recognition |
Abbreviated Journal |
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Issue |
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Pages |
453-458 |
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In this paper we propose an approach for word spotting in handwritten document images. We state the problem from a focused retrieval perspective, i.e. locating instances of a query word in a large scale dataset of digitized manuscripts. We combine two approaches, namely one based on word segmentation and another one segmentation-free. The first approach uses a hashing strategy to coarsely prune word images that are unlikely to be instances of the query word. This process is fast but has a low precision due to the errors introduced in the segmentation step. The regions containing candidate words are sent to the second process based on a state of the art technique from the visual object detection field. This discriminative model represents the appearance of the query word and computes a similarity score. In this way we propose a coarse-to-fine approach achieving a compromise between efficiency and accuracy. The validation of the model is shown using a collection of old handwritten manuscripts. We appreciate a substantial improvement in terms of precision regarding the previous proposed method with a low computational cost increase. |
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978-1-4673-2262-1 |
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ICFHR |
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DAG |
Approved |
no |
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DAG @ dag @ AFF2012 |
Serial |
1983 |
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| Author |
Marçal Rusiñol; Josep Llados |
| Title |
The Role of the Users in Handwritten Word Spotting Applications: Query Fusion and Relevance Feedback |
Type |
Conference Article |
| Year |
2012 |
Publication |
13th International Conference on Frontiers in Handwriting Recognition |
Abbreviated Journal |
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Issue |
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Pages |
55-60 |
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In this paper we present the importance of including the user in the loop in a handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and a baseline word spotting approach based on a bag-of-visual-words model. |
| Address |
Bari, Italy |
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978-1-4673-2262-1 |
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ICFHR |
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DAG |
Approved |
no |
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Admin @ si @ RuL2012 |
Serial |
2054 |
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| Author |
Volkmar Frinken; Markus Baumgartner; Andreas Fischer; Horst Bunke |
| Title |
Semi-Supervised Learning for Cursive Handwriting Recognition using Keyword Spotting |
Type |
Conference Article |
| Year |
2012 |
Publication |
13th International Conference on Frontiers in Handwriting Recognition |
Abbreviated Journal |
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Issue |
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Pages |
49-54 |
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State-of-the-art handwriting recognition systems are learning-based systems that require large sets of training data. The creation of training data, and consequently the creation of a well-performing recognition system, requires therefore a substantial amount of human work. This can be reduced with semi-supervised learning, which uses unlabeled text lines for training as well. Current approaches estimate the correct transcription of the unlabeled data via handwriting recognition which is not only extremely demanding as far as computational costs are concerned but also requires a good model of the target language. In this paper, we propose a different approach that makes use of keyword spotting, which is significantly faster and does not need any language model. In a set of experiments we demonstrate its superiority over existing approaches. |
| Address |
Bari, Italy |
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10.1109/ICFHR.2012.268 |
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978-1-4673-2262-1 |
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ICFHR |
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DAG |
Approved |
no |
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Admin @ si @ FBF2012 |
Serial |
2055 |
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| Author |
Emanuel Indermühle; Volkmar Frinken; Horst Bunke |
| Title |
Mode Detection in Online Handwritten Documents using BLSTM Neural Networks |
Type |
Conference Article |
| Year |
2012 |
Publication |
13th International Conference on Frontiers in Handwriting Recognition |
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Issue |
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Pages |
302-307 |
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| Abstract |
Mode detection in online handwritten documents refers to the process of distinguishing different types of contents, such as text, formulas, diagrams, or tables, one from another. In this paper a new approach to mode detection is proposed that uses bidirectional long-short term memory (BLSTM) neural networks. The BLSTM neural network is a novel type of recursive neural network that has been successfully applied in speech and handwriting recognition. In this paper we show that it has the potential to significantly outperform traditional methods for mode detection, which are usually based on stroke classification. As a further advantage over previous approaches, the proposed system is trainable and does not rely on user-defined heuristics. Moreover, it can be easily adapted to new or additional types of modes by just providing the system with new training data. |
| Address |
Bari, italy |
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978-1-4673-2262-1 |
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ICFHR |
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DAG |
Approved |
no |
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Admin @ si @ IFB2012 |
Serial |
2056 |
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| Author |
David Fernandez; Josep Llados; Alicia Fornes; R.Manmatha |
| Title |
On Influence of Line Segmentation in Efficient Word Segmentation in Old Manuscripts |
Type |
Conference Article |
| Year |
2012 |
Publication |
13th International Conference on Frontiers in Handwriting Recognition |
Abbreviated Journal |
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Issue |
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Pages |
763-768 |
| Keywords |
document image processing;handwritten character recognition;history;image segmentation;Spanish document;historical document;line segmentation;old handwritten document;old manuscript;word segmentation;Bifurcation;Dynamic programming;Handwriting recognition;Image segmentation;Measurement;Noise;Skeleton;Segmentation;document analysis;document and text processing;handwriting analysis;heuristics;path-finding |
| Abstract |
he objective of this work is to show the importance of a good line segmentation to obtain better results in the segmentation of words of historical documents. We have used the approach developed by Manmatha and Rothfeder [1] to segment words in old handwritten documents. In their work the lines of the documents are extracted using projections. In this work, we have developed an approach to segment lines more efficiently. The new line segmentation algorithm tackles with skewed, touching and noisy lines, so it is significantly improves word segmentation. Experiments using Spanish documents from the Marriages Database of the Barcelona Cathedral show that this approach reduces the error rate by more than 20% |
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978-1-4673-2262-1 |
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ICFHR |
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DAG |
Approved |
no |
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Admin @ si @ FLF2012 |
Serial |
2200 |
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| Author |
Dani Rowe; Ignasi Rius; Jordi Gonzalez; Juan J. Villanueva |
| Title |
Robust Particle Filtering for Object Tracking |
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Miscellaneous |
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2005 |
Publication |
13th International Conference on Image Analysis and Processing (ICIAP’2005), LNCS 3617: 1158–1165, ISBN 3–540–28869–4 |
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Cagliary (Italy) |
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ISE @ ise @ RRG2005e |
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577 |
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| Author |
Carlo Gatta; Simone Balocco; Francesco Ciompi; R. Hemetsberger; O. Rodriguez-Leor; Petia Radeva |
| Title |
Real-time gating of IVUS sequences based on motion blur analysis: Method and quantitative validation |
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Conference Article |
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2010 |
Publication |
13th international conference on Medical image computing and computer-assisted intervention |
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II |
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59-67 |
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| Abstract |
Intravascular Ultrasound (IVUS) is an image-guiding technique for cardiovascular diagnostic, providing cross-sectional images of vessels. During the acquisition, the catheter is pulled back (pullback) at a constant speed in order to acquire spatially subsequent images of the artery. However, during this procedure, the heart twist produces a swinging fluctuation of the probe position along the vessel axis. In this paper we propose a real-time gating algorithm based on the analysis of motion blur variations during the IVUS sequence. Quantitative tests performed on an in-vitro ground truth data base shown that our method is superior to state of the art algorithms both in computational speed and accuracy. |
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Springer-Verlag Berlin |
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MICCAI |
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MILAB |
Approved |
no |
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BCNPCL @ bcnpcl @ GBC2010 |
Serial |
1447 |
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| Author |
David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin |
| Title |
Virtual Worlds and Active Learning for Human Detection |
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Conference Article |
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2011 |
Publication |
13th International Conference on Multimodal Interaction |
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393-400 |
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Pedestrian Detection; Human detection; Virtual; Domain Adaptation; Active Learning |
| Abstract |
Image based human detection is of paramount interest due to its potential applications in fields such as advanced driving assistance, surveillance and media analysis. However, even detecting non-occluded standing humans remains a challenge of intensive research. The most promising human detectors rely on classifiers developed in the discriminative paradigm, i.e., trained with labelled samples. However, labeling is a manual intensive step, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, some authors have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of rendered images, i.e., using realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera, or similar ones. Accordingly, in this paper we address the challenge of using a virtual world for gathering (while playing a videogame) a large amount of automatically labelled samples (virtual humans and background) and then training a classifier that performs equal, in real-world images, than the one obtained by equally training from manually labelled real-world samples. For doing that, we cast the problem as one of domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we propose a non-standard active learning technique. Therefore, ultimately our human model is learnt by the combination of virtual and real world labelled samples (Fig. 1), which has not been done before. We present quantitative results showing that this approach is valid. |
| Address |
Alicante, Spain |
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Thesis |
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| Publisher |
ACM DL |
Place of Publication |
New York, NY, USA, USA |
Editor |
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| Language |
English |
Summary Language |
English |
Original Title |
Virtual Worlds and Active Learning for Human Detection |
| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
978-1-4503-0641-6 |
Medium |
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| Area |
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Expedition |
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Conference |
ICMI |
| Notes |
ADAS |
Approved |
yes |
| Call Number |
ADAS @ adas @ VLP2011a |
Serial |
1683 |
| Permanent link to this record |
