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Author |
Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
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Title |
Spotting Graphical Symbols in Camera-Acquired Documents in Real Time |
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Conference Article |
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Year |
2013 |
Publication |
10th IAPR International Workshop on Graphics Recognition |
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In this paper we present a system devoted to spot graphical symbols in camera-acquired document images. The system is based on the extraction and further matching of ORB compact local features computed over interest key-points. Then, the FLANN indexing framework based on approximate nearest neighbor search allows to efficiently match local descriptors between the captured scene and the graphical models. Finally, the RANSAC algorithm is used in order to compute the homography between the spotted symbol and its appearance in the document image. The proposed approach is efficient and is able to work in real time. |
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Bethlehem; PA; USA; August 2013 |
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GREC |
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Notes |
DAG; 600.045; 600.055; 600.061; 602.101 |
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Admin @ si @ RKL2013 |
Serial |
2347 |
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Author |
Marçal Rusiñol; T.Benkhelfallah; V. Poulain d'Andecy |
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Title |
Field Extraction from Administrative Documents by Incremental Structural Templates |
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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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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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Admin @ si @ RBP2013 |
Serial |
2346 |
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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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DAG; 600.056; 602.101 |
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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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Pages |
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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Call Number |
Admin @ si @ RRF2013 |
Serial |
2344 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
Boosting the Handwritten Word Spotting Experience by Including the User in the Loop |
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Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition |
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PR |
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Volume |
47 |
Issue |
3 |
Pages |
1063–1072 |
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Keywords |
Handwritten word spotting; Query by example; Relevance feedback; Query fusion; Multidimensional scaling |
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Abstract |
In this paper, we study the effect of taking the user into account in a query-by-example 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 two baseline word spotting approaches both based on the bag-of-visual-words model. We finally present two alternative ways of presenting the results to the user that might be more attractive and suitable to the user's needs than the classic ranked list. |
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0031-3203 |
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Notes |
DAG; 600.045; 600.061; 600.077 |
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Call Number |
Admin @ si @ RuL2013 |
Serial |
2343 |
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Author |
Marçal Rusiñol; Lluis Pere de las Heras; Oriol Ramos Terrades |
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Title |
Flowchart Recognition for Non-Textual Information Retrieval in Patent Search |
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Journal Article |
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Year |
2014 |
Publication |
Information Retrieval |
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IR |
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Volume |
17 |
Issue |
5-6 |
Pages |
545-562 |
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Flowchart recognition; Patent documents; Text/graphics separation; Raster-to-vector conversion; Symbol recognition |
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Abstract |
Relatively little research has been done on the topic of patent image retrieval and in general in most of the approaches the retrieval is performed in terms of a similarity measure between the query image and the images in the corpus. However, systems aimed at overcoming the semantic gap between the visual description of patent images and their conveyed concepts would be very helpful for patent professionals. In this paper we present a flowchart recognition method aimed at achieving a structured representation of flowchart images that can be further queried semantically. The proposed method was submitted to the CLEF-IP 2012 flowchart recognition task. We report the obtained results on this dataset. |
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ISSN |
1386-4564 |
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DAG; 600.077 |
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Admin @ si @ RHR2013 |
Serial |
2342 |
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Author |
Ernest Valveny; Oriol Ramos Terrades; Joan Mas; Marçal Rusiñol |
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Title |
Interactive Document Retrieval and Classification. |
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Book Chapter |
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Year |
2013 |
Publication |
Multimodal Interaction in Image and Video Applications |
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48 |
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17-30 |
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In this chapter we describe a system for document retrieval and classification following the interactive-predictive framework. In particular, the system addresses two different scenarios of document analysis: document classification based on visual appearance and logo detection. These two classical problems of document analysis are formulated following the interactive-predictive model, taking the user interaction into account to make easier the process of annotating and labelling the documents. A system implementing this model in a real scenario is presented and analyzed. This system also takes advantage of active learning techniques to speed up the task of labelling the documents. |
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Springer Berlin Heidelberg |
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Angel Sappa; Jordi Vitria |
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1868-4394 |
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978-3-642-35931-6 |
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DAG |
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Admin @ si @ VRM2013 |
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2341 |
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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 |
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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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Abstract |
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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Call Number |
Admin @ si @ BGR2013a |
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2332 |
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Author |
Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny |
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Title |
Handwritten Word Spotting with Corrected Attributes |
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Conference Article |
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Year |
2013 |
Publication |
15th IEEE International Conference on Computer Vision |
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1017-1024 |
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We propose an approach to multi-writer word spotting, where the goal is to find a query word in a dataset comprised of document images. We propose an attributes-based approach that leads to a low-dimensional, fixed-length representation of the word images that is fast to compute and, especially, fast to compare. This approach naturally leads to an unified representation of word images and strings, which seamlessly allows one to indistinctly perform query-by-example, where the query is an image, and query-by-string, where the query is a string. We also propose a calibration scheme to correct the attributes scores based on Canonical Correlation Analysis that greatly improves the results on a challenging dataset. We test our approach on two public datasets showing state-of-the-art results. |
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Sydney; Australia; December 2013 |
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1550-5499 |
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ICCV |
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DAG |
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Call Number |
Admin @ si @ AGF2013 |
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2327 |
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