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Author |
Lluis Pere de las Heras; Oriol Ramos Terrades; Sergi Robles; Gemma Sanchez |
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Title |
CVC-FP and SGT: a new database for structural floor plan analysis and its groundtruthing tool |
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Year |
2015 |
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International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
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18 |
Issue |
1 |
Pages |
15-30 |
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Recent results on structured learning methods have shown the impact of structural information in a wide range of pattern recognition tasks. In the field of document image analysis, there is a long experience on structural methods for the analysis and information extraction of multiple types of documents. Yet, the lack of conveniently annotated and free access databases has not benefited the progress in some areas such as technical drawing understanding. In this paper, we present a floor plan database, named CVC-FP, that is annotated for the architectural objects and their structural relations. To construct this database, we have implemented a groundtruthing tool, the SGT tool, that allows to make specific this sort of information in a natural manner. This tool has been made for general purpose groundtruthing: It allows to define own object classes and properties, multiple labeling options are possible, grants the cooperative work, and provides user and version control. We finally have collected some of the recent work on floor plan interpretation and present a quantitative benchmark for this database. Both CVC-FP database and the SGT tool are freely released to the research community to ease comparisons between methods and boost reproducible research. |
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Springer Berlin Heidelberg |
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1433-2833 |
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DAG; ADAS; 600.061; 600.076; 600.077 |
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Admin @ si @ HRR2015 |
Serial |
2567 |
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Author |
Christophe Rigaud; Clement Guerin; Dimosthenis Karatzas; Jean-Christophe Burie; Jean-Marc Ogier |
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Title |
Knowledge-driven understanding of images in comic books |
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Journal Article |
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Year |
2015 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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18 |
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3 |
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199-221 |
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Document Understanding; comics analysis; expert system |
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Document analysis is an active field of research, which can attain a complete understanding of the semantics of a given document. One example of the document understanding process is enabling a computer to identify the key elements of a comic book story and arrange them according to a predefined domain knowledge. In this study, we propose a knowledge-driven system that can interact with bottom-up and top-down information to progressively understand the content of a document. We model the comic book’s and the image processing domains knowledge for information consistency analysis. In addition, different image processing methods are improved or developed to extract panels, balloons, tails, texts, comic characters and their semantic relations in an unsupervised way. |
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Springer Berlin Heidelberg |
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1433-2833 |
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DAG; 600.056; 600.077 |
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RGK2015 |
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2595 |
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David Aldavert; Marçal Rusiñol; Ricardo Toledo; Josep Llados |
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Title |
A Study of Bag-of-Visual-Words Representations for Handwritten Keyword Spotting |
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Journal Article |
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2015 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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18 |
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3 |
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223-234 |
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Bag-of-Visual-Words; Keyword spotting; Handwritten documents; Performance evaluation |
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The Bag-of-Visual-Words (BoVW) framework has gained popularity among the document image analysis community, specifically as a representation of handwritten words for recognition or spotting purposes. Although in the computer vision field the BoVW method has been greatly improved, most of the approaches in the document image analysis domain still rely on the basic implementation of the BoVW method disregarding such latest refinements. In this paper, we present a review of those improvements and its application to the keyword spotting task. We thoroughly evaluate their impact against a baseline system in the well-known George Washington dataset and compare the obtained results against nine state-of-the-art keyword spotting methods. In addition, we also compare both the baseline and improved systems with the methods presented at the Handwritten Keyword Spotting Competition 2014. |
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Springer Berlin Heidelberg |
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1433-2833 |
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DAG; ADAS; 600.055; 600.061; 601.223; 600.077; 600.097 |
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Admin @ si @ ART2015 |
Serial |
2679 |
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Author |
Lluis Gomez; Dimosthenis Karatzas |
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Title |
A fast hierarchical method for multi‐script and arbitrary oriented scene text extraction |
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Journal Article |
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Year |
2016 |
Publication |
International Journal on Document Analysis and Recognition |
Abbreviated Journal |
IJDAR |
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19 |
Issue |
4 |
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335-349 |
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Keywords |
scene text; segmentation; detection; hierarchical grouping; perceptual organisation |
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Typography and layout lead to the hierarchical organisation of text in words, text lines, paragraphs. This inherent structure is a key property of text in any script and language, which has nonetheless been minimally leveraged by existing text detection methods. This paper addresses the problem of text
segmentation in natural scenes from a hierarchical perspective.
Contrary to existing methods, we make explicit use of text structure, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypotheses with
high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Results obtained over four standard datasets, covering text in variable orientations and different languages, demonstrate that our algorithm, while being trained in a single mixed dataset, outperforms state of the art
methods in unconstrained scenarios. |
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DAG; 600.056; 601.197 |
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Admin @ si @ GoK2016a |
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2862 |
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Author |
Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta |
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Title |
Large-scale graph indexing using binary embeddings of node contexts for information spotting in document image databases |
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Journal Article |
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Year |
2017 |
Publication |
Pattern Recognition Letters |
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PRL |
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Volume |
87 |
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Pages |
203-211 |
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Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations. However, retrieving a query graph from a large dataset of graphs implies a high computational complexity. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. With this aim, in this paper we propose a graph indexation formalism applied to visual retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Then, each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in different real scenarios such as handwritten word spotting in images of historical documents or symbol spotting in architectural floor plans. |
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DAG; 600.097; 602.006; 603.053; 600.121 |
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Call Number |
RLF2017b |
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2873 |
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Author |
Lluis Gomez; Dimosthenis Karatzas |
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Title |
TextProposals: a Text‐specific Selective Search Algorithm for Word Spotting in the Wild |
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Journal Article |
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Year |
2017 |
Publication |
Pattern Recognition |
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PR |
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70 |
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60-74 |
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Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (Goel et al., 2013; Almazán et al., 2014; Jaderberg et al., 2016) object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way.
Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers (Almazán et al., 2014; Jaderberg et al., 2016) shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10% F-score the best-performing method in the last ICDAR Robust Reading Competition (Karatzas, 2015). Source code of the complete end-to-end system is available at https://github.com/lluisgomez/TextProposals. |
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DAG; 600.084; 601.197; 600.121; 600.129 |
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Admin @ si @ GoK2017 |
Serial |
2886 |
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Author |
Lluis Gomez; Anguelos Nicolaou; Dimosthenis Karatzas |
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Title |
Improving patch‐based scene text script identification with ensembles of conjoined networks |
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2017 |
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Pattern Recognition |
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PR |
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67 |
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85-96 |
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DAG; 600.084; 600.121; 600.129 |
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Admin @ si @ GNK2017 |
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2887 |
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Author |
Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal |
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Product graph-based higher order contextual similarities for inexact subgraph matching |
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2018 |
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Pattern Recognition |
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PR |
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76 |
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596-611 |
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Many algorithms formulate graph matching as an optimization of an objective function of pairwise quantification of nodes and edges of two graphs to be matched. Pairwise measurements usually consider local attributes but disregard contextual information involved in graph structures. We address this issue by proposing contextual similarities between pairs of nodes. This is done by considering the tensor product graph (TPG) of two graphs to be matched, where each node is an ordered pair of nodes of the operand graphs. Contextual similarities between a pair of nodes are computed by accumulating weighted walks (normalized pairwise similarities) terminating at the corresponding paired node in TPG. Once the contextual similarities are obtained, we formulate subgraph matching as a node and edge selection problem in TPG. We use contextual similarities to construct an objective function and optimize it with a linear programming approach. Since random walk formulation through TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities and better discrimination among the nodes and edges. Experimental results shown on synthetic as well as real benchmarks illustrate that higher order contextual similarities increase discriminating power and allow one to find approximate solutions to the subgraph matching problem. |
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DAG; 602.167; 600.097; 600.121 |
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Admin @ si @ DLB2018 |
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3083 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Sparse representation over learned dictionary for symbol recognition |
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2016 |
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Signal Processing |
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SP |
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125 |
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36-47 |
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Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points |
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In this paper we propose an original sparse vector model for symbol retrieval task. More speci cally, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols. |
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DAG; 600.061; 600.077 |
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Admin @ si @ DTR2016 |
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2946 |
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Marçal Rusiñol; J. Chazalon; Katerine Diaz |
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Augmented Songbook: an Augmented Reality Educational Application for Raising Music Awareness |
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2018 |
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Multimedia Tools and Applications |
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MTAP |
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77 |
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11 |
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13773-13798 |
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Augmented reality; Document image matching; Educational applications |
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This paper presents the development of an Augmented Reality mobile application which aims at sensibilizing young children to abstract concepts of music. Such concepts are, for instance, the musical notation or the idea of rhythm. Recent studies in Augmented Reality for education suggest that such technologies have multiple benefits for students, including younger ones. As mobile document image acquisition and processing gains maturity on mobile platforms, we explore how it is possible to build a markerless and real-time application to augment the physical documents with didactic animations and interactive virtual content. Given a standard image processing pipeline, we compare the performance of different local descriptors at two key stages of the process. Results suggest alternatives to the SIFT local descriptors, regarding result quality and computational efficiency, both for document model identification and perspective transform estimation. All experiments are performed on an original and public dataset we introduce here. |
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DAG; ADAS; 600.084; 600.121; 600.118; 600.129 |
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Admin @ si @ RCD2018 |
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2996 |
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