A symbol spotting approach in graphical documents by hashing serialized graphs
Pattern Recognition
Building a symbol library from technical drawings by identifying repeating patterns
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
Bag-of-GraphPaths descriptors for symbol recognition and spotting in line drawings
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
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We present a content spotting system for line drawing graphic document images. The proposed system is sufficiently domain independent and takes the keyword based information retrieval for graphic documents, one step forward, to Query By Example (QBE) and focused retrieval. During offline learning mode: we vectorize the documents in the repository, represent them by attributed relational graphs, extract regions of interest (ROIs) from them, convert each ROI to a fuzzy structural signature, cluster similar signatures to form ROI classes and build an index for the repository. During online querying mode: a Bayesian network classifier recognizes the ROIs in the query image and the corresponding documents are fetched by looking up in the repository index. Experimental results are presented for synthetic images of architectural and electronic documents.