Segmentation of heterogeneous document images : an ... - Tel

Appendix A
Performance evaluation
methods
tel-00912566, version 1 - 2 Dec 2013
Many segmentation algorithms have been proposed in the literature. However
without a common performance evaluation method, it is hard to compare the
performance of these methods. Care should be taken in using a performance
metric to predict how well a segmentation method will perform on a particular
task.
Three popular performance evaluation methods are:
1. Precision and recall
2. Match counting
3. Scenario driven region correspondence
A.1 Precision and recall
Precision and recall are two measures that evaluate the results of a classification
task. The precision for a class is the number of true positive 1 divided by
the total number of objects that are labeled as belonging to the positive class.
Recall is defined as the number of true positives divided by the total number of
objects that actually belong to the positive class. In simple terms, high recall
means that a the classifier returned most of the relevant results. High precision
means that a classifier returned more relevant results than irrelevant.
In the context of text and graphics separation, a positive class is text and a
negative class is graphics. We define four counts:
• tp (true positive) is the number of correctly classified objects as text.
• fp (false positive) is the number of unexpected text objects.
• tn (true negative) is the number of correctly objects as graphics.
1 the number of objects that are labeled correctly as belonging to the positive class
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