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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados |
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Title |
Text line extraction in graphical documents using background and foreground |
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Journal Article |
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Year |
2012 |
Publication |
International Journal on Document Analysis and Recognition |
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IJDAR |
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15 |
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3 |
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227-241 |
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Abstract |
0,405 JCR
In graphical documents (e.g., maps, engineering drawings), artistic documents etc., the text lines are annotated in multiple orientations or curvilinear way to illustrate different locations or symbols. For the optical character recognition of such documents, individual text lines from the documents need to be extracted. In this paper, we propose a novel method to segment such text lines and the method is based on the foreground and background information of the text components. To effectively utilize the background information, a water reservoir concept is used here. In the proposed scheme, at first, individual components are detected and grouped into character clusters in a hierarchical way using size and positional information. Next, the clusters are extended in two extreme sides to determine potential candidate regions. Finally, with the help of these candidate regions,
individual lines are extracted. The experimental results are presented on different datasets of graphical documents, camera-based warped documents, noisy images containing seals, etc. The results demonstrate that our approach is robust and invariant to size and orientation of the text lines present in
the document. |
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1433-2833 |
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Admin @ si @ RPL2012b |
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2134 |
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Author |
Jaume Gibert |
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Title |
Vector Space Embedding of Graphs via Statistics of Labelling Information |
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Book Whole |
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Year |
2012 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Pattern recognition is the task that aims at distinguishing objects among different classes. When such a task wants to be solved in an automatic way a crucial step is how to formally represent such patterns to the computer. Based on the different representational formalisms, we may distinguish between statistical and structural pattern recognition. The former describes objects as a set of measurements arranged in the form of what is called a feature vector. The latter assumes that relations between parts of the underlying objects need to be explicitly represented and thus it uses relational structures such as graphs for encoding their inherent information. Vector spaces are a very flexible mathematical structure that has allowed to come up with several efficient ways for the analysis of patterns under the form of feature vectors. Nevertheless, such a representation cannot explicitly cope with binary relations between parts of the objects and it is restricted to measure the exact same number of features for each pattern under study regardless of their complexity. Graph-based representations present the contrary situation. They can easily adapt to the inherent complexity of the patterns but introduce a problem of high computational complexity, hindering the design of efficient tools to process and analyse patterns.
Solving this paradox is the main goal of this thesis. The ideal situation for solving pattern recognition problems would be to represent the patterns using relational structures such as graphs, and to be able to use the wealthy repository of data processing tools from the statistical pattern recognition domain. An elegant solution to this problem is to transform the graph domain into a vector domain where any processing algorithm can be applied. In other words, by mapping each graph to a point in a vector space we automatically get access to the rich set of algorithms from the statistical domain to be applied in the graph domain. Such methodology is called graph embedding.
In this thesis we propose to associate feature vectors to graphs in a simple and very efficient way by just putting attention on the labelling information that graphs store. In particular, we count frequencies of node labels and of edges between labels. Although their locality, these features are able to robustly represent structurally global properties of graphs, when considered together in the form of a vector. We initially deal with the case of discrete attributed graphs, where features are easy to compute. The continuous case is tackled as a natural generalization of the discrete one, where rather than counting node and edge labelling instances, we count statistics of some representatives of them. We encounter how the proposed vectorial representations of graphs suffer from high dimensionality and correlation among components and we face these problems by feature selection algorithms. We also explore how the diversity of different embedding representations can be exploited in order to boost the performance of base classifiers in a multiple classifier systems framework. An extensive experimental evaluation finally shows how the methodology we propose can be efficiently computed and compete with other graph matching and embedding methodologies. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Ernest Valveny |
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Admin @ si @ Gib2012 |
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2204 |
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Author |
Jaume Gibert |
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Title |
Learning structural representations and graph matching paradigms in the context of object recognition |
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Report |
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2009 |
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CVC Technical Report |
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143 |
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Computer Vision Center |
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Master's thesis |
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Admin @ si @ Gib2009 |
Serial |
2397 |
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Author |
Farshad Nourbakhsh |
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Title |
Colour logo recognition |
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Report |
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2009 |
Publication |
CVC Technical Report |
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145 |
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Computer Vision Center |
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Master's thesis |
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Bellaterra, Barcelona |
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Admin @ si @ Nou2009 |
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2399 |
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Author |
Marçal Rusiñol; K. Bertet; Jean-Marc Ogier; Josep Llados |
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Title |
Symbol Recognition Using a Concept Lattice of Graphical Patterns |
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Book Chapter |
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Year |
2010 |
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Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers |
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Volume |
6020 |
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Pages |
187-198 |
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Abstract |
In this paper we propose a new approach to recognize symbols by the use of a concept lattice. We propose to build a concept lattice in terms of graphical patterns. Each model symbol is decomposed in a set of composing graphical patterns taken as primitives. Each one of these primitives is described by boundary moment invariants. The obtained concept lattice relates which symbolic patterns compose a given graphical symbol. A Hasse diagram is derived from the context and is used to recognize symbols affected by noise. We present some preliminary results over a variation of the dataset of symbols from the GREC 2005 symbol recognition contest. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-13727-3 |
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DAG |
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no |
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Call Number |
Admin @ si @ RBO2010 |
Serial |
2407 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados |
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Title |
Touching Text Character Localization in Graphical Documents using SIFT |
Type |
Book Chapter |
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Year |
2010 |
Publication |
Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers |
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6020 |
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199-211 |
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Keywords |
Support Vector Machine; Text Component; Graphical Line; Document Image; Scale Invariant Feature Transform |
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Abstract |
Interpretation of graphical document images is a challenging task as it requires proper understanding of text/graphics symbols present in such documents. Difficulties arise in graphical document recognition when text and symbol overlapped/touched. Intersection of text and symbols with graphical lines and curves occur frequently in graphical documents and hence separation of such symbols is very difficult.
Several pattern recognition and classification techniques exist to recognize isolated text/symbol. But, the touching/overlapping text and symbol recognition has not yet been dealt successfully. An interesting technique, Scale Invariant Feature Transform (SIFT), originally devised for object recognition can take care of overlapping problems. Even if SIFT features have emerged as a very powerful object descriptors, their employment in graphical documents context has not been investigated much. In this paper we present the adaptation of the SIFT approach in the context of text character localization (spotting) in graphical documents. We evaluate the applicability of this technique in such documents and discuss the scope of improvement by combining some state-of-the-art approaches. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-13727-3 |
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DAG |
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Call Number |
Admin @ si @ RPL2010c |
Serial |
2408 |
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Author |
Nuria Cirera |
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Title |
Recognition of Handwritten Historical Documents |
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Report |
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2012 |
Publication |
CVC Technical Report |
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174 |
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Master's thesis |
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Admin @ si @ Cir2012 |
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2416 |
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Author |
T.Chauhan; E.Perales; Kaida Xiao; E.Hird ; Dimosthenis Karatzas; Sophie Wuerger |
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Title |
The achromatic locus: Effect of navigation direction in color space |
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Journal Article |
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2014 |
Publication |
Journal of Vision |
Abbreviated Journal |
VSS |
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14 (1) |
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25 |
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1-11 |
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Keywords |
achromatic; unique hues; color constancy; luminance; color space |
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Abstract |
5Y Impact Factor: 2.99 / 1st (Ophthalmology)
An achromatic stimulus is defined as a patch of light that is devoid of any hue. This is usually achieved by asking observers to adjust the stimulus such that it looks neither red nor green and at the same time neither yellow nor blue. Despite the theoretical and practical importance of the achromatic locus, little is known about the variability in these settings. The main purpose of the current study was to evaluate whether achromatic settings were dependent on the task of the observers, namely the navigation direction in color space. Observers could either adjust the test patch along the two chromatic axes in the CIE u*v* diagram or, alternatively, navigate along the unique-hue lines. Our main result is that the navigation method affects the reliability of these achromatic settings. Observers are able to make more reliable achromatic settings when adjusting the test patch along the directions defined by the four unique hues as opposed to navigating along the main axes in the commonly used CIE u*v* chromaticity plane. This result holds across different ambient viewing conditions (Dark, Daylight, Cool White Fluorescent) and different test luminance levels (5, 20, and 50 cd/m2). The reduced variability in the achromatic settings is consistent with the idea that internal color representations are more aligned with the unique-hue lines than the u* and v* axes. |
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DAG; 600.077 |
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no |
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Call Number |
Admin @ si @ CPX2014 |
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2418 |
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Author |
Antonio Clavelli; Dimosthenis Karatzas; Josep Llados; Mario Ferraro; Giuseppe Boccignone |
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Title |
Modelling task-dependent eye guidance to objects in pictures |
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Journal Article |
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Year |
2014 |
Publication |
Cognitive Computation |
Abbreviated Journal |
CoCom |
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6 |
Issue |
3 |
Pages |
558-584 |
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Visual attention; Gaze guidance; Value; Payoff; Stochastic fixation prediction |
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Abstract |
5Y Impact Factor: 1.14 / 3rd (Computer Science, Artificial Intelligence)
We introduce a model of attentional eye guidance based on the rationale that the deployment of gaze is to be considered in the context of a general action-perception loop relying on two strictly intertwined processes: sensory processing, depending on current gaze position, identifies sources of information that are most valuable under the given task; motor processing links such information with the oculomotor act by sampling the next gaze position and thus performing the gaze shift. In such a framework, the choice of where to look next is task-dependent and oriented to classes of objects embedded within pictures of complex scenes. The dependence on task is taken into account by exploiting the value and the payoff of gazing at certain image patches or proto-objects that provide a sparse representation of the scene objects. The different levels of the action-perception loop are represented in probabilistic form and eventually give rise to a stochastic process that generates the gaze sequence. This way the model also accounts for statistical properties of gaze shifts such as individual scan path variability. Results of the simulations are compared either with experimental data derived from publicly available datasets and from our own experiments. |
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Springer US |
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1866-9956 |
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DAG; 600.056; 600.045; 605.203; 601.212; 600.077 |
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Admin @ si @ CKL2014 |
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2419 |
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Author |
Miquel Ferrer; I. Bardaji; Ernest Valveny; Dimosthenis Karatzas; Horst Bunke |
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Title |
Median Graph Computation by Means of Graph Embedding into Vector Spaces |
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2013 |
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Graph Embedding for Pattern Analysis |
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45-72 |
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In pattern recognition [8, 14], a key issue to be addressed when designing a system is how to represent input patterns. Feature vectors is a common option. That is, a set of numerical features describing relevant properties of the pattern are computed and arranged in a vector form. The main advantages of this kind of representation are computational simplicity and a well sound mathematical foundation. Thus, a large number of operations are available to work with vectors and a large repository of algorithms for pattern analysis and classification exist. However, the simple structure of feature vectors might not be the best option for complex patterns where nonnumerical features or relations between different parts of the pattern become relevant. |
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Springer New York |
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Yun Fu; Yungian Ma |
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978-1-4614-4456-5 |
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Admin @ si @ FBV2013 |
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2421 |
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