Objective evaluation of binarization methods for document images

Several measures are implemented to evaluate the output of the binarization methods.

Description

% This function can be used to evaluate objectively the performance of binarization methods for document image.
%
% May 17th, 2010, By Reza Farrahi Moghaddam, Synchromedia Lab, ETS,
% Montreal, Canada
%
% The implemented measures are as follows [1]:
% Precision
% Recall
% Fmeasure (used as one of the measures in "Document Image Binarization Contest" (DIBCO'09) in ICDAR'09)
% Sensitivity (the same as Recall)
% Specificity
% BCR: The balanced classification rate
% AUC (The same as BCR)
% BER: The balanced error rate
% SFmeasure: F-measure based on sensitivity and specificity
% Accuracy
% GAccuracy: Geometric mean of sens and spec (to be used as the measure in "Quantitative evaluation of binarization algorithms of images of historical documents with bleeding noise" contest in ICFHR'10)
%
% [1] M. Sokolova and G. Lapalme, A systematic analysis of performance
% measures for classification tasks, Information Processing & Management,
% 45, pp. 427-437, 2009. DOI: 10.1016/j.ipm.2009.03.002
%
%
% USAGE:
% temp_obj_eval = objective_evaluation_core(u, u0_GT);
% where
% u is the input binarized image to be evaluated.
% u0_GT is the ground-truth binarized image.
% temp_obj_eval is the output. The measures can be reached as the fields of temp_obj_eval. For example:
% fprintf('Precision = %9.5f\n', temp_obj_eval.Precision);
%

• MATLAB release: MATLAB 7.5 (R2007b)