Spatio-structural Symbol Description with Statistical Feature Add-on

The original publication is available at www.springerlink.comInternational audienceIn this paper, we present a method for symbol description based on both spatio-structural and statistical features computed on elementary visual parts, called 'vocabulary'. This extracted vocabulary is grouped by type (e.g., circle, corner ) and serves as a basis for an attributed relational graph where spatial relational descriptors formalise the links between the vertices, formed by these types, labelled with global shape descriptors. The obtained attributed relational graph description has interesting properties that allows it to be used efficiently for recognising structure and by comparing its attribute signatures. The method is experimentally validated in the context of electrical symbol recognition from wiring diagrams

Spatio-structural Symbol Description with Statistical Feature Add-on

International audienceIn this paper, we present a method for symbol description based on spatio-structural as well as statistical features of visual elementary parts called 'vocabulary'. The extracted vocabulary is first organised into different groups based on their types (e.g., circle, corner). This vocabulary is used as a basis for an Attributed Relational Graph (ARG) where spatial relational descriptors formalise the links between the types, labelled with global shape descriptors. The description is used to globally recognise structure by comparing the signatures. The method is experimentally validated in the context of electrical symbol recognition from wiring diagrams

Symbol Recognition using Spatial Relations

International audienceIn this paper, we present a method for symbol recognition based on the spatio-structural description of a 'vocabulary' of extracted visual elementary parts. It is applied to symbols in electrical wiring diagrams. The method consists of first identifying vocabulary elements into different groups based on their types (e.g., circle, corner ). We then compute spatial relations between the possible pairs of labelled vocabulary types which are further used as a basis for building an Attributed Relational Graph that fully describes the symbol. These spatial relations integrate both topology and directional information. The experiments reported in this paper show that this approach, used for recognition, significantly outperforms both structural and signal-based state-of-the-art methods

Integrating Vocabulary Clustering with Spatial Relations for Symbol Recognition

International audienceThis paper develops a structural symbol recognition method with integrated statistical features. It applies spatial organization descriptors to the identified shape features within a fixed visual vocabulary that compose a symbol. It builds an attributed relational graph expressing the spatial relations between those visual vocabulary elements. In order to adapt the chosen vocabulary features to multiple and possible specialized contexts, we study the pertinence of unsupervised clustering to capture significant shape variations within a vocabulary class and thus refine the discriminative power of the method. This unsupervised clustering relies on cross-validation between several different cluster indices. The resulting approach is capable of determining part of the pertinent vocabulary and significantly increases recognition results with respect to the state-of-the-art. It is experimentally validated on complex electrical wiring diagram symbols

Pattern recognition methods for querying and browsing technical documentation

Abstract. Graphics recognition deals with the specific pattern recognition problems found in graphics-rich documents, typical technical documentation of all kinds. In this paper, we propose a short journey through 20 years of involvement and contributions within this scientific community, and explore more precisely a few interesting issues found when the problem is to browse, query and navigate in a large and complex set of technical documents. Key words: graphics recognition, symbol recognition, document analysis, information spotting