Word spotting using radial descriptor graph

Majeed Kassis, Jihad El-Sana

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2016 15th International Conference on Frontiers in Handwriting Recognition, ICFHR 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages31-35
Number of pages5
ISBN (Electronic)9781509009817
DOIs
StatePublished - 2 Jul 2016
Event15th International Conference on Frontiers in Handwriting Recognition, ICFHR 2016 - Shenzhen, China
Duration: 23 Oct 201626 Oct 2016

Publication series

NameProceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR
Volume0
ISSN (Print)2167-6445
ISSN (Electronic)2167-6453

Conference

Conference15th International Conference on Frontiers in Handwriting Recognition, ICFHR 2016
Country/TerritoryChina
CityShenzhen
Period23/10/1626/10/16

Keywords

  • Graph
  • Learning-free
  • Local feature
  • Radial descriptor

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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