A Bivariate Autoregressive Technique for Analysis and Classification of Planar Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Complex Autoregressive Model for Shape Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching for Shape Defect Detection
CAIP '99 Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns
Spotting Symbols in Line Drawing Images Using Graph Representations
Graphics Recognition. Recent Advances and New Opportunities
Building Synthetic Graphical Documents for Performance Evaluation
Graphics Recognition. Recent Advances and New Opportunities
International Journal on Document Analysis and Recognition
Relational indexing of vectorial primitives for symbol spotting in line-drawing images
Pattern Recognition Letters
Symbol spotting in vectorized technical drawings through a lookup table of region strings
Pattern Analysis & Applications
A Content Spotting System for Line Drawing Graphic Document Images
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A bag-of-paths based serialized subgraph matching for symbol spotting in line drawings
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Fourier Coding of Image Boundaries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symbol Spotting in Line Drawings through Graph Paths Hashing
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
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Graphical symbol recognition and spotting recently have become an important research activity. In this work we present a descriptor for symbols, especially for line drawings. The descriptor is based on the graph representation of graphical objects. We construct graphs from the vectorized information of the binarized images, where the critical points detected by the vectorization algorithm are considered as nodes and the lines joining them are considered as edges. Graph paths between two nodes in a graph are the finite sequences of nodes following the order from the starting to the final node. The occurrences of different graph paths in a given graph is an important feature, as they capture the geometrical and structural attributes of a graph. So the graph representing a symbol can efficiently be represent by the occurrences of its different paths. Their occurrences in a symbol can be obtained in terms of a histogram counting the number of some fixed prototype paths, we call the histogram as the Bag-of-GraphPaths (BOGP). These BOGP histograms are used as a descriptor to measure the distance among the symbols in vector space. We use the descriptor for three applications, they are: (1) classification of the graphical symbols, (2) spotting of the architectural symbols on floorplans, (3) classification of the historical handwritten words.