Symbol descriptor based on shape context and vector model of information retrieval

International audienceIn this paper we present an adaptative method for graphic symbol representation based on shape contexts. The proposed descriptor is invariant under classical geometric transforms (rotation, scale) and based on interest points. To reduce the complexity of matching a symbol to a large set of candidates we use the popular vector model for information retrieval. In this way, on the set of shape descriptors we build a visual vocabulary where each symbol is retrieved on visual words. Experimental results on complex and occluded symbols show that the approach is very promising

New Approach for Symbol Recognition Combining Shape Context of Interest Points with Sparse Representation

International audienceIn this paper, we propose a new approach for symbol description. Our method is built based on the combination of shape context of interest points descriptor and sparse representation. More specifically, we first learn a dictionary describing shape context of interest point descriptors. Then, based on information retrieval techniques, we build a vector model for each symbol based on its sparse representation in a visual vocabulary whose visual words are columns in the learned dictionary. The retrieval task is performed by ranking symbols based on similarity between vector models. The evaluation of our method, using benchmark datasets, demonstrates the validity of our approach and shows that it outperforms related state-of-theart methods

Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier

We present a new approach for recognition of complex graphic symbols in technical documents. Graphic symbol recognition is a well known challenge in the field of document image analysis and is at heart of most graphic recognition systems. Our method uses structural approach for symbol representation and statistical classifier for symbol recognition. In our system we represent symbols by their graph based signatures: a graphic symbol is vectorized and is converted to an attributed relational graph, which is used for computing a feature vector for the symbol. This signature corresponds to geometry and topology of the symbol. We learn a Bayesian network to encode joint probability distribution of symbol signatures and use it in a supervised learning scenario for graphic symbol recognition. We have evaluated our method on synthetically deformed and degraded images of pre-segmented 2D architectural and electronic symbols from GREC databases and have obtained encouraging recognition rates.Comment: 5 pages, 8 figures, Tenth International Conference on Document Analysis and Recognition (ICDAR), IEEE Computer Society, 2009, volume 10, 1325-132

DTW-Radon-based Shape Descriptor for Pattern Recognition

International audienceIn this paper, we present a pattern recognition method that uses dynamic programming (DP) for the alignment of Radon features. The key characteristic of the method is to use dynamic time warping (DTW) to match corresponding pairs of the Radon features for all possible projections. Thanks to DTW, we avoid compressing the feature matrix into a single vector which would otherwise miss information. To reduce the possible number of matchings, we rely on a initial normalisation based on the pattern orientation. A comprehensive study is made using major state-of-the-art shape descriptors over several public datasets of shapes such as graphical symbols (both printed and hand-drawn), handwritten characters and footwear prints. In all tests, the method proves its generic behaviour by providing better recognition performance. Overall, we validate that our method is robust to deformed shape due to distortion, degradation and occlusion

Improving Information Retrieval in Multiwriter Scenario by Exploiting the Similarity Graph of Document Terms

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordInformation Retrieval (IR) is the activity of obtaining information resources relevant to a questioned information. It usually retrieves a set of objects ranked according to the relevancy to the needed fact. In document analysis, information retrieval receives a lot of attention in terms of symbol and word spotting. However, through decades the community mostly focused either on printed or on single writer scenario, where the state-of-The-art results have achieved reasonable performance on the available datasets. Nevertheless, the existing algorithms do not perform accordingly on multiwriter scenario. A graph representing relations between a set of objects is a structure where each node delineates an individual element and the similarity between them is represented as a weight on the connecting edge. In this paper, we explore different analytics of graphs constructed from words or graphical symbols, such as diffusion, shortest path, etc. to improve the performance of information retrieval methods in multiwriter scenario.European Union Horizon 2020Ministerio de Educación, Cultura y Deporte, SpainFPUCERCA Programme/Generalitat de Cataluny

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