Fast computation of moment invariants
Pattern Recognition
Pattern recognition with moment invariants: a comparative study and new results
Pattern Recognition
A complete invariant description for gray-level images by the harmonic analysis approach
Pattern Recognition Letters
Symbol Recognition by Error-Tolerant Subgraph Matching between Region Adjacency Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Learning and Recognition of Graphical Symbols in Engineering Drawings
Selected Papers from the First International Workshop on Graphics Recognition, Methods and Applications
A Structural Representation Adapted to Handwritten Symbol Recognition
GREC '99 Selected Papers from the Third International Workshop on Graphics Recognition, Recent Advances
Engineering Drawings Recognition Using a Case-based Approach
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Computer Vision and Image Understanding
A Region-Based Hashing Approach for Symbol Spotting in Technical Documents
Graphics Recognition. Recent Advances and New Opportunities
Pattern Recognition Methods for Querying and Browsing Technical Documentation
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Relational indexing of vectorial primitives for symbol spotting in line-drawing images
Pattern Recognition Letters
Circular blurred shape model for symbol spotting in documents
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
GREC'05 Proceedings of the 6th international conference on Graphics Recognition: ten Years Review and Future Perspectives
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In this paper we propose a new approach to find symbols in graphical documents. The method is based on a representation of the document in chain points extracted from the skeleton. We merge successively these chain points into a dendrogram framework and according to a measure of density. From the dendrogram, we extract potential symbols which can be recognized after.