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Author |
Joan Mas; Josep Llados; Gemma Sanchez; J.A. Jorge |
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Title |
A syntactic approach based on distortion-tolerant Adjacency Grammars and a spatial-directed parser to interpret sketched diagrams |
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2010 |
Publication |
Pattern Recognition |
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PR |
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43 |
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12 |
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4148–4164 |
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Syntactic Pattern Recognition; Symbol recognition; Diagram understanding; Sketched diagrams; Adjacency Grammars; Incremental parsing; Spatial directed parsing |
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This paper presents a syntactic approach based on Adjacency Grammars (AG) for sketch diagram modeling and understanding. Diagrams are a combination of graphical symbols arranged according to a set of spatial rules defined by a visual language. AG describe visual shapes by productions defined in terms of terminal and non-terminal symbols (graphical primitives and subshapes), and a set functions describing the spatial arrangements between symbols. Our approach to sketch diagram understanding provides three main contributions. First, since AG are linear grammars, there is a need to define shapes and relations inherently bidimensional using a sequential formalism. Second, our parsing approach uses an indexing structure based on a spatial tessellation. This serves to reduce the search space when finding candidates to produce a valid reduction. This allows order-free parsing of 2D visual sentences while keeping combinatorial explosion in check. Third, working with sketches requires a distortion model to cope with the natural variations of hand drawn strokes. To this end we extended the basic grammar with a distortion measure modeled on the allowable variation on spatial constraints associated with grammar productions. Finally, the paper reports on an experimental framework an interactive system for sketch analysis. User tests performed on two real scenarios show that our approach is usable in interactive settings. |
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Elsevier |
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DAG @ dag @ MLS2010 |
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1336 |
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Author |
Umapada Pal; Partha Pratim Roy; N. Tripathya; Josep Llados |
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Title |
Multi-oriented Bangla and Devnagari text recognition |
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Journal Article |
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2010 |
Publication |
Pattern Recognition |
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PR |
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43 |
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12 |
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4124–4136 |
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There are printed complex documents where text lines of a single page may have different orientations or the text lines may be curved in shape. As a result, it is difficult to detect the skew of such documents and hence character segmentation and recognition of such documents are a complex task. In this paper, using background and foreground information we propose a novel scheme towards the recognition of Indian complex documents of Bangla and Devnagari script. In Bangla and Devnagari documents usually characters in a word touch and they form cavity regions. To take care of these cavity regions, background information of such documents is used. Convex hull and water reservoir principle have been applied for this purpose. Here, at first, the characters are segmented from the documents using the background information of the text. Next, individual characters are recognized using rotation invariant features obtained from the foreground part of the characters.
For character segmentation, at first, writing mode of a touching component (word) is detected using water reservoir principle based features. Next, depending on writing mode and the reservoir base-region of the touching component, a set of candidate envelope points is then selected from the contour points of the component. Based on these candidate points, the touching component is finally segmented into individual characters. For recognition of multi-sized/multi-oriented characters the features are computed from different angular information obtained from the external and internal contour pixels of the characters. These angular information are computed in such a way that they do not depend on the size and rotation of the characters. Circular and convex hull rings have been used to divide a character into smaller zones to get zone-wise features for higher recognition results. We combine circular and convex hull features to improve the results and these features are fed to support vector machines (SVM) for recognition. From our experiment we obtained recognition results of 99.18% (98.86%) accuracy when tested on 7515 (7874) Devnagari (Bangla) characters. |
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DAG @ dag @ PRT2010 |
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1337 |
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Josep Llados; Horst Bunke; Enric Marti |
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Title |
Finding rotational symmetries by cyclic string matching |
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Year |
1997 |
Publication |
Pattern recognition letters |
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PRL |
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18 |
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14 |
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1435-1442 |
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Rotational symmetry; Reflectional symmetry; String matching |
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Symmetry is an important shape feature. In this paper, a simple and fast method to detect perfect and distorted rotational symmetries of 2D objects is described. The boundary of a shape is polygonally approximated and represented as a string. Rotational symmetries are found by cyclic string matching between two identical copies of the shape string. The set of minimum cost edit sequences that transform the shape string to a cyclically shifted version of itself define the rotational symmetry and its order. Finally, a modification of the algorithm is proposed to detect reflectional symmetries. Some experimental results are presented to show the reliability of the proposed algorithm |
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DAG;IAM; |
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IAM @ iam @ LBM1997a |
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1562 |
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Author |
Josep Llados; Enric Marti |
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Title |
A graph-edit algorithm for hand-drawn graphical document recognition and their automatic introduction into CAD systems |
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Journal Article |
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1999 |
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Machine Graphics & Vision |
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8 |
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195-211 |
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DAG;IAM; |
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IAM @ iam @ LIM1999 |
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1568 |
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Josep Llados; Enric Marti |
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Graph-edit algorithms for hand-drawn graphical document recognition and their automatic introduction |
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1999 |
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Machine Graphics & Vision journal, special issue on Graph transformation |
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DAG;IAM |
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IAM @ iam @ LIM1999c |
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1569 |
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Author |
Josep Llados; Ernest Valveny; Enric Marti |
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Title |
Symbol Recognition in Document Image Analysis: Methods and Challenges |
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Journal Article |
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2000 |
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Recent Research Developments in Pattern Recognition, Transworld Research Network, |
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1 |
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151–178. |
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81-86846-61-1 |
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DAG;IAM |
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IAM @ iam @ LVM2000 |
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1575 |
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Author |
Josep Llados; Enric Marti; Juan J.Villanueva |
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Title |
Symbol recognition by error-tolerant subgraph matching between region adjacency graphs |
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Journal Article |
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2001 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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23 |
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10 |
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1137-1143 |
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The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content. |
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DAG;IAM;ISE; |
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IAM @ iam @ LMV2001 |
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1581 |
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Ernest Valveny; Enric Marti |
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A model for image generation and symbol recognition through the deformation of lineal shapes |
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2003 |
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Pattern Recognition Letters |
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PRL |
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24 |
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15 |
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2857-2867 |
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We describe a general framework for the recognition of distorted images of lineal shapes, which relies on three items: a model to represent lineal shapes and their deformations, a model for the generation of distorted binary images and the combination of both models in a common probabilistic framework, where the generation of deformations is related to an internal energy, and the generation of binary images to an external energy. Then, recognition consists in the minimization of a global energy function, performed by using the EM algorithm. This general framework has been applied to the recognition of hand-drawn lineal symbols in graphic documents. |
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Elsevier Science Inc. |
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New York, NY, USA |
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0167-8655 |
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DAG; IAM |
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IAM @ iam @ VAM2003 |
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1653 |
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Ernest Valveny; Enric Marti |
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Title |
Deformable Template Matching within a Bayesian Framework for Hand-Written Graphic Symbol Recognition |
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2000 |
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Graphics Recognition Recent Advances |
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1941 |
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193-208 |
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We describe a method for hand-drawn symbol recognition based on deformable template matching able to handle uncertainty and imprecision inherent to hand-drawing. Symbols are represented as a set of straight lines and their deformations as geometric transformations of these lines. Matching, however, is done over the original binary image to avoid loss of information during line detection. It is defined as an energy minimization problem, using a Bayesian framework which allows to combine fidelity to ideal shape of the symbol and flexibility to modify the symbol in order to get the best fit to the binary input image. Prior to matching, we find the best global transformation of the symbol to start the recognition process, based on the distance between symbol lines and image lines. We have applied this method to the recognition of dimensions and symbols in architectural floor plans and we show its flexibility to recognize distorted symbols. |
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Springer Verlag |
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Springer Verlag |
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DAG;IAM; |
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IAM @ iam @ MVA2000 |
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1655 |
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Author |
Lluis Pere de las Heras; Ahmed Sheraz; Marcus Liwicki; Ernest Valveny; Gemma Sanchez |
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Title |
Statistical Segmentation and Structural Recognition for Floor Plan Interpretation |
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Journal Article |
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2014 |
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International Journal on Document Analysis and Recognition |
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IJDAR |
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17 |
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3 |
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221-237 |
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A generic method for floor plan analysis and interpretation is presented in this article. The method, which is mainly inspired by the way engineers draw and interpret floor plans, applies two recognition steps in a bottom-up manner. First, basic building blocks, i.e., walls, doors, and windows are detected using a statistical patch-based segmentation approach. Second, a graph is generated, and structural pattern recognition techniques are applied to further locate the main entities, i.e., rooms of the building. The proposed approach is able to analyze any type of floor plan regardless of the notation used. We have evaluated our method on different publicly available datasets of real architectural floor plans with different notations. The overall detection and recognition accuracy is about 95 %, which is significantly better than any other state-of-the-art method. Our approach is generic enough such that it could be easily adopted to the recognition and interpretation of any other printed machine-generated structured documents. |
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Springer Berlin Heidelberg |
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1433-2833 |
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DAG; ADAS; 600.076; 600.077 |
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HSL2014 |
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2370 |
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