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Author |
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 |
Publication |
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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ICDAR |
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DAG; 600.056; 602.101 |
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no |
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Call Number |
Admin @ si @ GRK2013c |
Serial |
2345 |
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Author |
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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Year |
2013 |
Publication |
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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1520-5363 |
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DAG |
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Admin @ si @ RRF2013 |
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2344 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
New Approach for Symbol Recognition Combining Shape Context of Interest Points with Sparse Representation |
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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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265-269 |
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In this paper, we propose a new approach for symbol description. Our method is built based on the combination of shape context of interest points descriptor and sparse representation. More specifically, we first learn a dictionary describing shape context of interest point descriptors. Then, based on information retrieval techniques, we build a vector model for each symbol based on its sparse representation in a visual vocabulary whose visual words are columns in the learneddictionary. The retrieval task is performed by ranking symbols based on similarity between vector models. Evaluation of our method, using benchmark datasets, demonstrates the validity of our approach and shows that it outperforms related state-of-theart methods. |
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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 @ DTR2013b |
Serial |
2331 |
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Author |
R. Bertrand; P. Gomez-Krämer; Oriol Ramos Terrades; P. Franco; Jean-Marc Ogier |
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Title |
A System Based On Intrinsic Features for Fraudulent Document Detection |
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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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106-110 |
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Keywords |
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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Notes |
DAG; 600.061 |
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Call Number |
Admin @ si @ BGR2013a |
Serial |
2332 |
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Author |
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 |
Publication |
12th International Conference on Document Analysis and Recognition |
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Pages |
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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Call Number |
Admin @ si @ CrT2013 |
Serial |
2329 |
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Author |
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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Year |
2013 |
Publication |
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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1520-5363 |
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ICDAR |
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DAG |
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Call Number |
Admin @ si @ AFV2013 |
Serial |
2326 |
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Author |
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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Pages |
1245-1249 |
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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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DAG; 600.061; 600.056; 600.045 |
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Call Number |
Admin @ si @ HFV2013 |
Serial |
2319 |
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Author |
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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2013 |
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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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ICDAR |
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DAG; 600.056 |
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Admin @ si @ KSU2013 |
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2318 |
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Author |
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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Year |
2013 |
Publication |
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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1520-5363 |
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ICDAR |
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DAG; 600.056; 601.158; 601.197 |
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Call Number |
Admin @ si @ GoK2013 |
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2310 |
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Author |
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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ICDAR |
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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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