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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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Pages |
265-269 |
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Abstract |
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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no |
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Call Number |
Admin @ si @ DTR2013b |
Serial |
2331 |
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Author |
Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny |
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Title |
Handwritten Word Spotting with Corrected Attributes |
Type |
Conference Article |
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Year |
2013 |
Publication |
15th IEEE International Conference on Computer Vision |
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Pages |
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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ISSN |
1550-5499 |
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Conference |
ICCV |
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DAG |
Approved |
no |
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Call Number |
Admin @ si @ AGF2013 |
Serial |
2327 |
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Author |
Francisco Cruz; Oriol Ramos Terrades |
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Title |
Handwritten Line Detection via an EM Algorithm |
Type |
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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no |
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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 |
Type |
Conference Article |
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Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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Issue |
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Pages |
1022-1026 |
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Abstract |
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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Address |
Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG |
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no |
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Call Number |
Admin @ si @ AFV2013 |
Serial |
2326 |
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Author |
Lluis Gomez |
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Title |
Perceptual Organization for Text Extraction in Natural Scenes |
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Report |
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Year |
2012 |
Publication |
CVC Technical Report |
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Volume |
173 |
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Address |
Bellaterra |
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Thesis |
Master's thesis |
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DAG |
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no |
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Call Number |
Admin @ si @ Gom2012 |
Serial |
2309 |
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Author |
Albert Gordo; Alicia Fornes; Ernest Valveny |
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Title |
Writer identification in handwritten musical scores with bags of notes |
Type |
Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
46 |
Issue |
5 |
Pages |
1337-1345 |
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Abstract |
Writer Identification is an important task for the automatic processing of documents. However, the identification of the writer in graphical documents is still challenging. In this work, we adapt the Bag of Visual Words framework to the task of writer identification in handwritten musical scores. A vanilla implementation of this method already performs comparably to the state-of-the-art. Furthermore, we analyze the effect of two improvements of the representation: a Bhattacharyya embedding, which improves the results at virtually no extra cost, and a Fisher Vector representation that very significantly improves the results at the cost of a more complex and costly representation. Experimental evaluation shows results more than 20 points above the state-of-the-art in a new, challenging dataset. |
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0031-3203 |
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DAG |
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no |
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Call Number |
Admin @ si @ GFV2013 |
Serial |
2307 |
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Author |
Antonio Clavelli; Dimosthenis Karatzas; Josep Llados; Mario Ferraro; Giuseppe Boccignone |
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Title |
Towards Modelling an Attention-Based Text Localization Process |
Type |
Conference Article |
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Year |
2013 |
Publication |
6th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
7887 |
Issue |
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Pages |
296-303 |
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Keywords |
text localization; visual attention; eye guidance |
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Abstract |
This note introduces a visual attention model of text localization in real-world scenes. The core of the model built upon the proto-object concept is discussed. It is shown how such dynamic mid-level representation of the scene can be derived in the framework of an action-perception loop engaging salience, text information value computation, and eye guidance mechanisms.
Preliminary results that compare model generated scanpaths with those eye-tracked from human subjects are presented. |
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Address |
Madeira; Portugal; June 2013 |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-38627-5 |
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IbPRIA |
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DAG |
Approved |
no |
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Call Number |
Admin @ si @ CKL2013 |
Serial |
2291 |
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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Embedding of Graphs with Discrete Attributes Via Label Frequencies |
Type |
Journal Article |
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Year |
2013 |
Publication |
International Journal of Pattern Recognition and Artificial Intelligence |
Abbreviated Journal |
IJPRAI |
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Volume |
27 |
Issue |
3 |
Pages |
1360002-1360029 |
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Keywords |
Discrete attributed graphs; graph embedding; graph classification |
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Abstract |
Graph-based representations of patterns are very flexible and powerful, but they are not easily processed due to the lack of learning algorithms in the domain of graphs. Embedding a graph into a vector space solves this problem since graphs are turned into feature vectors and thus all the statistical learning machinery becomes available for graph input patterns. In this work we present a new way of embedding discrete attributed graphs into vector spaces using node and edge label frequencies. The methodology is experimentally tested on graph classification problems, using patterns of different nature, and it is shown to be competitive to state-of-the-art classification algorithms for graphs, while being computationally much more efficient. |
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DAG |
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no |
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Call Number |
Admin @ si @ GVB2013 |
Serial |
2305 |
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Author |
Albert Gordo |
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Title |
Document Image Representation, Classification and Retrieval in Large-Scale Domains |
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Book Whole |
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Year |
2013 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Despite the “paperless office” ideal that started in the decade of the seventies, businesses still strive against an increasing amount of paper documentation. Companies still receive huge amounts of paper documentation that need to be analyzed and processed, mostly in a manual way. A solution for this task consists in, first, automatically scanning the incoming documents. Then, document images can be analyzed and information can be extracted from the data. Documents can also be automatically dispatched to the appropriate workflows, used to retrieve similar documents in the dataset to transfer information, etc.
Due to the nature of this “digital mailroom”, we need document representation methods to be general, i.e., able to cope with very different types of documents. We need the methods to be sound, i.e., able to cope with unexpected types of documents, noise, etc. And, we need to methods to be scalable, i.e., able to cope with thousands or millions of documents that need to be processed, stored, and consulted. Unfortunately, current techniques of document representation, classification and retrieval are not apt for this digital mailroom framework, since they do not fulfill some or all of these requirements.
Through this thesis we focus on the problem of document representation aimed at classification and retrieval tasks under this digital mailroom framework. We first propose a novel document representation based on runlength histograms, and extend it to cope with more complex documents such as multiple-page documents, or documents that contain more sources of information such as extracted OCR text. Then we focus on the scalability requirements and propose a novel binarization method which we dubbed PCAE, as well as two general asymmetric distances between binary embeddings that can significantly improve the retrieval results at a minimal extra computational cost. Finally, we note the importance of supervised learning when performing large-scale retrieval, and study several approaches that can significantly boost the results at no extra cost at query time. |
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Address |
Barcelona |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
Ernest Valveny;Florent Perronnin |
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DAG |
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no |
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Call Number |
Admin @ si @ Gor2013 |
Serial |
2277 |
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Author |
Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados |
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Title |
Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces |
Type |
Book Chapter |
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Year |
2013 |
Publication |
Graph Embedding for Pattern Analysis |
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Pages |
1-26 |
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Abstract |
Ability to recognize patterns is among the most crucial capabilities of human beings for their survival, which enables them to employ their sophisticated neural and cognitive systems [1], for processing complex audio, visual, smell, touch, and taste signals. Man is the most complex and the best existing system of pattern recognition. Without any explicit thinking, we continuously compare, classify, and identify huge amount of signal data everyday [2], starting from the time we get up in the morning till the last second we fall asleep. This includes recognizing the face of a friend in a crowd, a spoken word embedded in noise, the proper key to lock the door, smell of coffee, the voice of a favorite singer, the recognition of alphabetic characters, and millions of more tasks that we perform on regular basis. |
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Springer New York |
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978-1-4614-4456-5 |
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DAG |
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no |
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Call Number |
Admin @ si @ LRL2013b |
Serial |
2271 |
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