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Objective evaluation of binarization methods for document images

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5.0 | 3 ratings Rate this file 11 Downloads (last 30 days) File Size: 1.57 MB File ID: #27652 Version: 1.3

Objective evaluation of binarization methods for document images

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18 May 2010 (Updated )

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

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Description

% This function can be used to evaluate objectively the performance of binarization methods for document image.
%
% December 27th, 2012, By Reza FARRAHI MOGHADDAM and Hossein ZIAEI NAFCHI, Synchromedia Lab, ETS, Montreal, Canada
% 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mean of sens and spec (to be used as the measure in "Quantitative evaluation of binarization algorithms of images of historical documents with bleeding noise" contest in ICFHR'10)
% pFMeasure: pseudo F-Measure
% NRM: Negative rate metric
% PSNR: Peak signal-to-noise ratio
% DRD: Distance reciprocal distortion metric [2]
% MPM: Misclassification penalty metric [3]
%
% [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
%
% [2] H. Lu, A. C. Kot, Y. Q. Shi, Distance-Reciprocal Distortion Measure
% for Binary Document Images, IEEE Signal Processing Letters, vol. 11,
% no. 2, pp. 228-231, 2004.
%
% [3] D. P. Young, J. M. Ferryman, PETS Metrics: On-Line Performance
% Evaluation Service, ICCCN '05 Proceedings of the 14th International
% Conference on Computer Communications and Networks, pp. 317-324, 2005.%
%
% USAGE:
% temp_obj_eval = objective_evaluation_core(u, u0_GT, u0_skl_GT);
% where
% u is: the input binarized image to be evaluated.
% u0_GT: is the ground-truth binarized image.
% u0_skl_GT: is an optional input for the ground-truth skeleton of u0_GT. If not specified, the skeleton is automatically calculated using the thininng method.
% 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 7.14 (R2012a)
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Comments and Ratings (6)
22 Sep 2016 Chandrashekar Gudada

thank you...

17 May 2016 V RAJINIKANTH

Thank you Sir, Works very well.

Comment only
26 Apr 2016 sindhuja annareddy

sir can u please provide code for how to find bleed through prasent in an document and how to remove it "phase based binarization of ancient document images"

Comment only
01 Mar 2016 mohamed abu eita

Many thanks

Comment only
27 Jan 2015 Yuvarajoo Subramaniam

Thanks for the file. It has been very useful to me.

05 Dec 2014 Vincent Dore  
Updates
28 Dec 2012 1.1

Adding five new measures:
1. pFMeasure: pseudo F-Measure
2. NRM: Negative rate metric
3. PSNR: Peak signal-to-noise ratio
4. DRD: Distance reciprocal distortion metric
5. MPM: Misclassification penalty metric

04 Jan 2013 1.2

Minor modification.

Adding five new measures:
1. pFMeasure: pseudo F-Measure
2. NRM: Negative rate metric
3. PSNR: Peak signal-to-noise ratio
4. DRD: Distance reciprocal distortion metric
5. MPM: Misclassification penalty metric

17 Sep 2013 1.3

Added:
1. A function to calculate the indicators and their statistics for a whole database.
2. An additional average F-measure and average p-F-measure according to the DIBCO series' definition.

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