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Author |
Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados |
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Title |
Towards Query-by-Speech Handwritten Keyword Spotting |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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501-505 |
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In this paper, we present a new querying paradigm for handwritten keyword spotting. We propose to represent handwritten word images both by visual and audio representations, enabling a query-by-speech keyword spotting system. The two representations are merged together and projected to a common sub-space in the training phase. This transform allows to, given a spoken query, retrieve word instances that were only represented by the visual modality. In addition, the same method can be used backwards at no additional cost to produce a handwritten text-tospeech system. We present our first results on this new querying mechanism using synthetic voices over the George Washington
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Nancy; France; August 2015 |
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DAG; 600.084; 600.061; 601.223; 600.077;ADAS |
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Admin @ si @ RAT2015b |
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2682 |
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Author |
Mathieu Nicolas Delalandre; Ernest Valveny; Josep Llados |
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Title |
Performance Evaluation of Symbol Recognition and Spotting Systems |
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Conference Article |
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2008 |
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Proceedings of the 8th International Workshop on Document Analysis Systems, |
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497–505 |
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Nara (Japan) |
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DAG |
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DAG @ dag @ DVL2008b |
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1060 |
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Author |
Josep Llados; Jaime Lopez-Krahe; Enric Marti |
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Title |
Hand drawn document understanding using the straight line Hough transform and graph matching |
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Conference Article |
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Year |
1996 |
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Proceedings of the 13th International Pattern Recognition Conference (ICPR’96) |
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2 |
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497-501 |
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This paper presents a system to understand hand drawn architectural drawings in a CAD environment. The procedure is to identify in a floor plan the building elements, stored in a library of patterns, and their spatial relationships. The vectorized input document and the patterns to recognize are represented by attributed graphs. To recognize the patterns as such, we apply a structural approach based on subgraph isomorphism techniques. In spite of their value, graph matching techniques do not recognize adequately those building elements characterized by hatching patterns, i.e. walls. Here we focus on the recognition of hatching patterns and develop a straight line Hough transform based method in order to detect the regions filled in with parallel straight fines. This allows not only to recognize filling patterns, but it actually reduces the computational load associated with the subgraph isomorphism computation. The result is that the document can be redrawn by editing all the patterns recognized |
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Vienna , Austria |
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DAG;IAM; |
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IAM @ iam @ LLM1996 |
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1579 |
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Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva |
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Title |
Circular Blurred Shape Model for Multiclass Symbol Recognition |
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2011 |
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IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE) |
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TSMCB |
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41 |
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2 |
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497-506 |
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In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code design. The results over four symbol data sets show the significant improvements of the proposed descriptor compared to the state-of-the-art descriptors. In particular, the results are even more significant in those cases where the symbols suffer from elastic deformations. |
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1083-4419 |
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MILAB; DAG;HuPBA |
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Admin @ si @ EFP2011 |
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1784 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
Word and Symbol Spotting using Spatial Organization of Local Descriptors |
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Conference Article |
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2008 |
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Proceedings of the 8th IAPR International Workshop on Document Analysis Systems, |
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489–496 |
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Nara (Japan) |
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DAG |
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DAG @ dag @ RuL2008b |
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1059 |
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Author |
Josep Llados; Marçal Rusiñol |
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Title |
Graphics Recognition Techniques |
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2014 |
Publication |
Handbook of Document Image Processing and Recognition |
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D |
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489-521 |
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Dimension recognition; Graphics recognition; Graphic-rich documents; Polygonal approximation; Raster-to-vector conversion; Texture-based primitive extraction; Text-graphics separation |
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This chapter describes the most relevant approaches for the analysis of graphical documents. The graphics recognition pipeline can be splitted into three tasks. The low level or lexical task extracts the basic units composing the document. The syntactic level is focused on the structure, i.e., how graphical entities are constructed, and involves the location and classification of the symbols present in the document. The third level is a functional or semantic level, i.e., it models what the graphical symbols do and what they mean in the context where they appear. This chapter covers the lexical level, while the next two chapters are devoted to the syntactic and semantic level, respectively. The main problems reviewed in this chapter are raster-to-vector conversion (vectorization algorithms) and the separation of text and graphics components. The research and industrial communities have provided standard methods achieving reasonable performance levels. Hence, graphics recognition techniques can be considered to be in a mature state from a scientific point of view. Additionally this chapter provides insights on some related problems, namely, the extraction and recognition of dimensions in engineering drawings, and the recognition of hatched and tiled patterns. Both problems are usually associated, even integrated, in the vectorization process. |
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Springer London |
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D. Doermann; K. Tombre |
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978-0-85729-858-4 |
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DAG; 600.077 |
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Admin @ si @ LlR2014 |
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2380 |
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Author |
Albert Gordo; Ernest Valveny |
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Title |
A rotation invariant page layout descriptor for document classification and retrieval |
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Conference Article |
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2009 |
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10th International Conference on Document Analysis and Recognition |
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481–485 |
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Document classification usually requires of structural features such as the physical layout to obtain good accuracy rates on complex documents. This paper introduces a descriptor of the layout and a distance measure based on the cyclic dynamic time warping which can be computed in O(n2). This descriptor is translation invariant and can be easily modified to be scale and rotation invariant. Experiments with this descriptor and its rotation invariant modification are performed on the Girona archives database and compared against another common layout distance, the minimum weight edge cover. The experiments show that these methods outperform the MWEC both in accuracy and speed, particularly on rotated documents. |
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Barcelona, Spain |
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1520-5363 |
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978-1-4244-4500-4 |
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ICDAR |
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DAG |
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DAG @ dag @ GoV2009a |
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1175 |
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Author |
Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados; R.Jain; D.Doermann |
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Title |
Novel Line Verification for Multiple Instance Focused Retrieval in Document Collections |
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Conference Article |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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481-485 |
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Nancy; France; August 2015 |
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DAG; 600.077; 601.223; 600.084; 600.061 |
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Admin @ si @ GRK2015 |
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2683 |
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Author |
Josep Llados; Enric Marti; Jaime Lopez-Krahe |
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A Hough-based method for hatched pattern detection in maps and diagrams |
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Conference Article |
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1999 |
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Proceeding of the Fifth Int. Conf. Document Analysis and Recognition ICDAR ’99 |
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479-482 |
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A hatched area is characterized by a set of parallel straight lines placed at regular intervals. In this paper, a Hough-based schema is introduced to recognize hatched areas in technical documents from attributed graph structures representing the document once it has been vectorized. Defining a Hough-based transform from a graph instead of the raster image allows to drastically reduce the processing time and, second, to obtain more reliable results because straight lines have already been detected in the vectorization step. A second advantage of the proposed method is that no assumptions must be made a priori about the slope and frequency of hatching patterns, but they are computed in run time for each hatched area. |
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DAG;IAM; |
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IAM @ iam @ LIM1999b |
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1580 |
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Author |
David Fernandez; Pau Riba; Alicia Fornes; Josep Llados |
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Title |
On the Influence of Key Point Encoding for Handwritten Word Spotting |
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Conference Article |
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2014 |
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14th International Conference on Frontiers in Handwriting Recognition |
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476 - 481 |
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Local descriptors; Interest points; Handwritten documents; Word spotting; Historical document analysis |
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In this paper we evaluate the influence of the selection of key points and the associated features in the performance of word spotting processes. In general, features can be extracted from a number of characteristic points like corners, contours, skeletons, maxima, minima, crossings, etc. A number of descriptors exist in the literature using different interest point detectors. But the intrinsic variability of handwriting vary strongly on the performance if the interest points are not stable enough. In this paper, we analyze the performance of different descriptors for local interest points. As benchmarking dataset we have used the Barcelona Marriage Database that contains handwritten records of marriages over five centuries. |
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Creete Island; Grecia; September 2014 |
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2167-6445 |
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978-1-4799-4335-7 |
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ICFHR |
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DAG; 600.056; 600.061; 602.006; 600.077 |
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Admin @ si @ FRF2014 |
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2460 |
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