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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Albert Gordo; Marçal Rusiñol; Dimosthenis Karatzas; Andrew Bagdanov |
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Title |
Document Classification and Page Stream Segmentation for Digital Mailroom Applications |
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Conference Article |
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Year |
2013 |
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12th International Conference on Document Analysis and Recognition |
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621-625 |
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In this paper we present a method for the segmentation of continuous page streams into multipage documents and the simultaneous classification of the resulting documents. We first present an approach to combine the multiple pages of a document into a single feature vector that represents the whole document. Despite its simplicity and low computational cost, the proposed representation yields results comparable to more complex methods in multipage document classification tasks. We then exploit this representation in the context of page stream segmentation. The most plausible segmentation of a page stream into a sequence of multipage documents is obtained by optimizing a statistical model that represents the probability of each segmented multipage document belonging to a particular class. Experimental results are reported on a large sample of real administrative multipage documents. |
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Washington; USA; August 2013 |
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1520-5363 |
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DAG; 600.056; 602.101 |
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Admin @ si @ GRK2013c |
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2345 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Alicia Fornes; Xavier Otazu; Josep Llados |
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Title |
Show through cancellation and image enhancement by multiresolution contrast processing |
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Conference Article |
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Year |
2013 |
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12th International Conference on Document Analysis and Recognition |
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200-204 |
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Historical documents suffer from different types of degradation and noise such as background variation, uneven illumination or dark spots. In case of double-sided documents, another common problem is that the back side of the document usually interferes with the front side because of the transparency of the document or ink bleeding. This effect is called the show through phenomenon. Many methods are developed to solve these problems, and in the case of show-through, by scanning and matching both the front and back sides of the document. In contrast, our approach is designed to use only one side of the scanned document. We hypothesize that show-trough are low contrast components, while foreground components are high contrast ones. A Multiresolution Contrast (MC) decomposition is presented in order to estimate the contrast of features at different spatial scales. We cancel the show-through phenomenon by thresholding these low contrast components. This decomposition is also able to enhance the image removing shadowed areas by weighting spatial scales. Results show that the enhanced images improve the readability of the documents, allowing scholars both to recover unreadable words and to solve ambiguities. |
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Washington; USA; August 2013 |
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1520-5363 |
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DAG; 602.006; 600.045; 600.061; 600.052;CIC |
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Admin @ si @ FOL2013 |
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2241 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Andreas Fischer; Volkmar Frinken; Horst Bunke; Ching Y. Suen |
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Title |
Improving HMM-Based Keyword Spotting with Character Language Models |
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Conference Article |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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506-510 |
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Facing high error rates and slow recognition speed for full text transcription of unconstrained handwriting images, keyword spotting is a promising alternative to locate specific search terms within scanned document images. We have previously proposed a learning-based method for keyword spotting using character hidden Markov models that showed a high performance when compared with traditional template image matching. In the lexicon-free approach pursued, only the text appearance was taken into account for recognition. In this paper, we integrate character n-gram language models into the spotting system in order to provide an additional language context. On the modern IAM database as well as the historical George Washington database, we demonstrate that character language models significantly improve the spotting performance. |
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Washington; USA; August 2013 |
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1520-5363 |
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DAG; 600.045; 605.203 |
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Admin @ si @ FFB2013 |
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2295 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal |
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Title |
Near Convex Region Adjacency Graph and Approximate Neighborhood String Matching for Symbol Spotting in Graphical Documents |
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Conference Article |
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2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
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1078-1082 |
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This paper deals with a subgraph matching problem in Region Adjacency Graph (RAG) applied to symbol spotting in graphical documents. RAG is a very important, efficient and natural way of representing graphical information with a graph but this is limited to cases where the information is well defined with perfectly delineated regions. What if the information we are interested in is not confined within well defined regions? This paper addresses this particular problem and solves it by defining near convex grouping of oriented line segments which results in near convex regions. Pure convexity imposes hard constraints and can not handle all the cases efficiently. Hence to solve this problem we have defined a new type of convexity of regions, which allows convex regions to have concavity to some extend. We call this kind of regions Near Convex Regions (NCRs). These NCRs are then used to create the Near Convex Region Adjacency Graph (NCRAG) and with this representation we have formulated the problem of symbol spotting in graphical documents as a subgraph matching problem. For subgraph matching we have used the Approximate Edit Distance Algorithm (AEDA) on the neighborhood string, which starts working after finding a key node in the input or target graph and iteratively identifies similar nodes of the query graph in the neighborhood of the key node. The experiments are performed on artificial, real and distorted datasets. |
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Washington; USA; August 2013 |
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1520-5363 |
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DAG; 600.045; 600.056; 600.061; 601.152 |
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Admin @ si @ DLB2013a |
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2358 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Christophe Rigaud; Dimosthenis Karatzas; Joost Van de Weijer; Jean-Christophe Burie; Jean-Marc Ogier |
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Title |
An active contour model for speech balloon detection in comics |
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Conference Article |
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Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
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1240-1244 |
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Comic books constitute an important cultural heritage asset in many countries. Digitization combined with subsequent comic book understanding would enable a variety of new applications, including content-based retrieval and content retargeting. Document understanding in this domain is challenging as comics are semi-structured documents, combining semantically important graphical and textual parts. Few studies have been done in this direction. In this work we detail a novel approach for closed and non-closed speech balloon localization in scanned comic book pages, an essential step towards a fully automatic comic book understanding. The approach is compared with existing methods for closed balloon localization found in the literature and results are presented. |
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washington; USA; August 2013 |
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1520-5363 |
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DAG; CIC; 600.056 |
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Admin @ si @ RKW2013a |
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2260 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
David Aldavert; Marçal Rusiñol; Ricardo Toledo; Josep Llados |
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Title |
Integrating Visual and Textual Cues for Query-by-String Word Spotting |
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Conference Article |
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Year |
2013 |
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12th International Conference on Document Analysis and Recognition |
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511 - 515 |
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In this paper, we present a word spotting framework that follows the query-by-string paradigm where word images are represented both by textual and visual representations. The textual representation is formulated in terms of character $n$-grams while the visual one is based on the bag-of-visual-words scheme. These two representations are merged together and projected to a sub-vector space. This transform allows to, given a textual query, retrieve word instances that were only represented by the visual modality. Moreover, this statistical representation can be used together with state-of-the-art indexation structures in order to deal with large-scale scenarios. The proposed method is evaluated using a collection of historical documents outperforming state-of-the-art performances. |
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Washington; USA; August 2013 |
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1520-5363 |
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DAG; ADAS; 600.045; 600.055; 600.061 |
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Admin @ si @ ART2013 |
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2224 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Dimosthenis Karatzas; Faisal Shafait; Seiichi Uchida; Masakazu Iwamura; Lluis Gomez; Sergi Robles; Joan Mas; David Fernandez; Jon Almazan; Lluis Pere de las Heras |
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Title |
ICDAR 2013 Robust Reading Competition |
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Conference Article |
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Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
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1484-1493 |
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This report presents the final results of the ICDAR 2013 Robust Reading Competition. The competition is structured in three Challenges addressing text extraction in different application domains, namely born-digital images, real scene images and real-scene videos. The Challenges are organised around specific tasks covering text localisation, text segmentation and word recognition. The competition took place in the first quarter of 2013, and received a total of 42 submissions over the different tasks offered. This report describes the datasets and ground truth specification, details the performance evaluation protocols used and presents the final results along with a brief summary of the participating methods. |
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Washington; USA; August 2013 |
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1520-5363 |
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DAG; 600.056 |
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Admin @ si @ KSU2013 |
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2318 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Francisco Cruz; Oriol Ramos Terrades |
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Title |
Handwritten Line Detection via an EM Algorithm |
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Conference Article |
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Year |
2013 |
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12th International Conference on Document Analysis and Recognition |
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718-722 |
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In this paper we present a handwritten line segmentation method devised to work on documents composed of several paragraphs with multiple line orientations. The method is based on a variation of the EM algorithm for the estimation of a set of regression lines between the connected components that compose the image. We evaluated our method on the ICDAR2009 handwriting segmentation contest dataset with promising results that overcome most of the presented methods. In addition, we prove the usability of the presented method by performing line segmentation on the George Washington database obtaining encouraging results. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG |
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Admin @ si @ CrT2013 |
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2329 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados; Tomokazu Sato; Masakazu Iwamura; Koichi Kise |
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Title |
Key-region detection for document images -applications to administrative document retrieval |
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Conference Article |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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230-234 |
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In this paper we argue that a key-region detector designed to take into account the special characteristics of document images can result in the detection of less and more meaningful key-regions. We propose a fast key-region detector able to capture aspects of the structural information of the document, and demonstrate its efficiency by comparing against standard detectors in an administrative document retrieval scenario. We show that using the proposed detector results to a smaller number of detected key-regions and higher performance without any drop in speed compared to standard state of the art detectors. |
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Washington; USA; August 2013 |
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1520-5363 |
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DAG; 600.056; 600.045 |
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Admin @ si @ GRK2013b |
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2293 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Jon Almazan; Alicia Fornes; Ernest Valveny |
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Title |
A Deformable HOG-based Shape Descriptor |
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Conference Article |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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1022-1026 |
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In this paper we deal with the problem of recognizing handwritten shapes. We present a new deformable feature extraction method that adapts to the shape to be described, dealing in this way with the variability introduced in the handwriting domain. It consists in a selection of the regions that best define the shape to be described, followed by the computation of histograms of oriented gradients-based features over these points. Our results significantly outperform other descriptors in the literature for the task of hand-drawn shape recognition and handwritten word retrieval |
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Washington; USA; August 2013 |
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DAG |
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Admin @ si @ AFV2013 |
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2326 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
L. Rothacker; Marçal Rusiñol; G.A. Fink |
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Title |
Bag-of-Features HMMs for segmentation-free word spotting in handwritten documents |
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Conference Article |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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1305 - 1309 |
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Recent HMM-based approaches to handwritten word spotting require large amounts of learning samples and mostly rely on a prior segmentation of the document. We propose to use Bag-of-Features HMMs in a patch-based segmentation-free framework that are estimated by a single sample. Bag-of-Features HMMs use statistics of local image feature representatives. Therefore they can be considered as a variant of discrete HMMs allowing to model the observation of a number of features at a point in time. The discrete nature enables us to estimate a query model with only a single example of the query provided by the user. This makes our method very flexible with respect to the availability of training data. Furthermore, we are able to outperform state-of-the-art results on the George Washington dataset. |
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Washington; USA; August 2013 |
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DAG |
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Admin @ si @ RRF2013 |
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2344 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Lluis Gomez; Dimosthenis Karatzas |
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Title |
Multi-script Text Extraction from Natural Scenes |
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Conference Article |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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467-471 |
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Scene text extraction methodologies are usually based in classification of individual regions or patches, using a priori knowledge for a given script or language. Human perception of text, on the other hand, is based on perceptual organisation through which text emerges as a perceptually significant group of atomic objects. Therefore humans are able to detect text even in languages and scripts never seen before. In this paper, we argue that the text extraction problem could be posed as the detection of meaningful groups of regions. We present a method built around a perceptual organisation framework that exploits collaboration of proximity and similarity laws to create text-group hypotheses. Experiments demonstrate that our algorithm is competitive with state of the art approaches on a standard dataset covering text in variable orientations and two languages. |
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Washington; USA; August 2013 |
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ICDAR |
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Notes |
DAG; 600.056; 601.158; 601.197 |
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Call Number |
Admin @ si @ GoK2013 |
Serial |
2310 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Lluis Pere de las Heras; David Fernandez; Ernest Valveny; Josep Llados; Gemma Sanchez |
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Title |
Unsupervised wall detector in architectural floor plan |
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Conference Article |
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Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
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1245-1249 |
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Abstract |
Wall detection in floor plans is a crucial step in a complete floor plan recognition system. Walls define the main structure of buildings and convey essential information for the detection of other structural elements. Nevertheless, wall segmentation is a difficult task, mainly because of the lack of a standard graphical notation. The existing approaches are restricted to small group of similar notations or require the existence of pre-annotated corpus of input images to learn each new notation. In this paper we present an automatic wall segmentation system, with the ability to handle completely different notations without the need of any annotated dataset. It only takes advantage of the general knowledge that walls are a repetitive element, naturally distributed within the plan and commonly modeled by straight parallel lines. The method has been tested on four datasets of real floor plans with different notations, and compared with the state-of-the-art. The results show its suitability for different graphical notations, achieving higher recall rates than the rest of the methods while keeping a high average precision. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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Notes |
DAG; 600.061; 600.056; 600.045 |
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no |
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Call Number |
Admin @ si @ HFV2013 |
Serial |
2319 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
M. Visani; V.C.Kieu; Alicia Fornes; N.Journet |
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Title |
The ICDAR 2013 Music Scores Competition: Staff Removal |
Type |
Conference Article |
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Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
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1439-1443 |
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The first competition on music scores that was organized at ICDAR in 2011 awoke the interest of researchers, who participated both at staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario: old music scores. For this purpose, we have generated a new set of images using two kinds of degradations: local noise and 3D distortions. This paper describes the dataset, distortion methods, evaluation metrics, the participant's methods and the obtained results. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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Notes |
DAG; 600.045; 600.061 |
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no |
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Call Number |
Admin @ si @ VKF2013 |
Serial |
2338 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Marçal Rusiñol; T.Benkhelfallah; V. Poulain d'Andecy |
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Title |
Field Extraction from Administrative Documents by Incremental Structural Templates |
Type |
Conference Article |
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Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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1100 - 1104 |
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In this paper we present an incremental framework aimed at extracting field information from administrative document images in the context of a Digital Mail-room scenario. Given a single training sample in which the user has marked which fields have to be extracted from a particular document class, a document model representing structural relationships among words is built. This model is incrementally refined as the system processes more and more documents from the same class. A reformulation of the tf-idf statistic scheme allows to adjust the importance weights of the structural relationships among words. We report in the experimental section our results obtained with a large dataset of real invoices. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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Notes |
DAG; 600.56; 600.045; 605.203; 602.101 |
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no |
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Call Number |
Admin @ si @ RBP2013 |
Serial |
2346 |
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