Symbol Recognition: Current Advances and Perspectives

Abstract. 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

Automatic Adjacency Grammar Generation from User Drawn Sketches

http://www.ieee.orgIn this paper we present an innovative approach to automatically generate adjacency grammars describing graphical symbols. A grammar production is formulated in terms of rulesets of geometrical constraints among symbol primitives. Given a set of symbol instances sketched by a user using a digital pen, our approach infers the grammar productions consisting of the ruleset most likely to occur. The performance of our work is evaluated using a comprehensive benchmarking database of on-line symbols

On the use of textural features for writer identification in old handwritten music scores

Writer identification consists in determining the writer of a piece of handwriting from a set of writers. In this paper we present a system for writer identification in old handwritten music scores which uses only music notation to determine the author. The steps of the proposed system are the following. First of all, the music sheet is preprocessed for obtaining a music score without the staff lines. Afterwards, four different methods for generating texture images from music symbols are applied. Every approach uses a different spatial variation when combining the music symbols to generate the textures. Finally, Gabor filters and Grey-scale Co-ocurrence matrices are used to obtain the features. The classification is performed using a k-NN classifier based on Euclidean distance. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving encouraging identification rates

L’animador/a sociocultural: apunts “sui generis” per a la seva definició actual

L’article ofereix algunes reflexions urgents al voltant de la praxi de l’animació sociocultural. Sense entrar en els continguts específics professionals que han de ser la base de l’ofici i que són impartits en els diversos centres universitaris, té una modesta intenció alliçonadora. Per il·lustrar-la, cada part s’ inicia amb el títol d’una obra literària referenciadora que és comentada després en referència a les preocupacions i neguits de la professió

An Error-Correction Graph Grammar to Recognize Textured Symbols

Colloque avec actes et comité de lecture. internationale.International audienceThis paper presents an algorithm for recognizing symbols with textured elements in a graphical document. A region adjacency graph represents the graphical document, with the nodes being polygons and the edges the neighborhood relations between them. The textured symbols are modeled by a graph, where nodes are polygons (represented by strings) or textured areas (represented by a graph grammar with error-correction rules). The recognition process is done by a graph matching process that uses a string edit distance to recognize the static parts of the symbol and a parsing process that segments the subgraph in the original graph, following the rules of the graph grammar

A Mean String Algorithm to Compute the Average Among a Set of 2D Shapes

Article dans revue scientifique avec comité de lecture.International audienceAn algorithm to compute the mean shape, when the shape is represented by a string, is presented as a modification of the well-known string edit algorithm. Given $N$ strings of symbols, a string edit sequence defines a mapping between their corresponding symbols. We transform these sets of mapped symbols (edges) into piecewise linear functions and we compute their mean. To transform them into functions, we use the equation of the line defining their edges, and the percentage of their length, in order to have a common parameterization. The algorithm has been experimentally tested in the computation of a representative among a class of shapes in a clustering procedure in the domain of a graphics recognition application

Old Handwritten Musical Symbol classification by a Dynamic Time Warping based method

Abstract. A growing interest in the document analysis field is the recognition of old handwritten documents, towards the conversion into a readable format. The difficulties when working with old documents are increased, and other techniques are required for recognizing handwritten graphical symbols that are drawn in such these documents. In this paper we present a Dynamic Time Warping based method that outperforms the classical descriptors, being also invariant to scale, rotation, and elastic deformations typical found in handwriting musical notation.

Categorization of digital ink elements using spectral features

Desriptors for digital ink are usually sequences of features that evolve with time. Since handwriting is an oscillatory process, it is reasonable to think that there is much information in the frequency spectra of such signals. In particular, ink elements of different nature, like text and graphics, might present very different spectra due to different gestural behaviours needed to draw them. Therefore, the descriptor we propose is the Fourier transform of the angle difference between successive ink segments. On a database containing text and symbols, an unsupervised clustering is performed based on this descriptor and clear clusters corresponding to text-only and graphic-only elements emerge