Klaus Broelemann2014
BookSection
Springer Berlin Heidelberg
2014
Graphics Recognition. Current Trends and Challenges
8746
25-37
Klaus Broelemann
Anjan Dutta
Xiaoyi Jiang
Josep Llados
Bart Lamiroy
Jean-Marc Ogier
Hierarchical Plausibility-Graphs for Symbol Spotting in Graphical Documents
LNCS
Graph representation of graphical documents often suffers from noise such as spurious nodes and edges, and their discontinuity. In general these errors occur during the low-level image processing viz. binarization, skeletonization, vectorization etc. Hierarchical graph representation is a nice and efficient way to solve this kind of problem by hierarchically merging node-node and node-edge depending on the distance. But the creation of hierarchical graph representing the graphical information often uses hard thresholds on the distance to create the hierarchical nodes (next state) of the lower nodes (or states) of a graph. As a result, the representation often loses useful information. This paper introduces plausibilities to the nodes of hierarchical graph as a function of distance and proposes a modified algorithm for matching subgraphs of the hierarchical graphs. The plausibility-annotated nodes help to improve the performance of the matching algorithm on two hierarchical structures. To show the potential of this approach, we conduct an experiment with the SESYD dataset.