Word Spotting Using Radial Descriptor Graph


In this paper we present, the Radial Descriptor Graph, a novel approach to compare pictorial representation of handwritten text, which is based on the radial descriptor. To build a radial descriptor graph, we compute the radial descriptor and generate feature points. These points are the nodes of the graph, and each adjacent points are connected to its adjacent node to form a planar graph. Then we iteratively reduce the edges of the graph, by merging adjacent nodes, to form a multilevel hierarchical representation of the graph. To compare two pictorial representations, we measure the distance between their correspondence planar graphs, after calculating the dominant signal for each node. The graph matching is based on optimizing the function that takes into account the distance between the feature points and the structure of the graphs. The distance between two radial descriptors is computed by measuring the difference between their corresponding dominant signals. We have tested our approach on three different datasets and obtained encouraging results. Keywords—local feature, radial descriptor, graph, learning-free

DOI: 10.1109/ICFHR.2016.0019

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@inproceedings{Kassis2016WordSU, title={Word Spotting Using Radial Descriptor Graph}, author={Majeed Kassis and Jihad El-Sana}, booktitle={ICFHR}, year={2016} }