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2016 23rd International Conference on Pattern Recognition (ICPR) Cancn Center, Cancn, Mxico, December 48, 2016Inexact Graph Matching for Entity Recognition in OCRed Documents Nihel Kooli and Abdel
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01
Identify the two graphs that you want to compare for similarity.
02
Determine the metrics that will be used to measure the similarity between the graphs.
03
Decide on a threshold for how close the graphs need to be in order to be considered a match.
04
Implement an algorithm or software tool that can perform inexact graph matching.
05
Run the inexact graph matching algorithm on the two graphs and analyze the results.

Who needs inexact graph matching for?

01
Researchers in the field of molecular biology who are comparing complex molecular structures.
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Computer vision experts who are analyzing patterns in images.
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Data scientists who are trying to find similarities in large datasets.
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Inexact graph matching is used to find correspondences between nodes and edges in different graphs when the graphs may not be identical but share similar structures or features.
Individuals or organizations that need to match data from multiple graphs, especially in fields like computer vision, social network analysis, or biological networks are required to engage in inexact graph matching.
To fill out inexact graph matching, one must define the graphs involved, specify the criteria for matching, and employ algorithms to identify correspondences based on the defined parameters.
The purpose of inexact graph matching is to enable the analysis and interpretation of related graph structures in various applications, allowing for flexible comparison and integration of data.
Information that must be reported includes the graphs being matched, method of matching, results of the matching process, and any discrepancies or additional insights gained from the analysis.
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