View source for Binarization of PHIBD 2012 dataset

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| '''Created: '''2013-05-30
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=Description=
Binarization of handwritten Document Images.

There are actually two tasks, depending on the nature of the binarization method used.

#	For regular binarization methods, the task is to binarize all 15 document images.
#	For learning-based binarization methods, the task is to use images number 1 to 5 for training, and then binarize images number 6 to 16.

A few baseline methods have been provided: PC (phase congruency) binarization method [Ziaei2012], and SGL/BGL binarization method [Farrahi2009, Farrahi2010]. The SGL/BGL method uses a rough binarization as its initialization.




=Evaluation Protocol=

#	For regular methods, the average F-measure of the binarized images against the provided ground truth is used as the performance of the binarization method in question.
#	For learning-based methods, the average F-measure of the binarized images number 6 to 15 against the provided ground truth is used as the performance of the binarization method in question.

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! Method
!Whole set
!Training set
!Test set
|- 
|Otsu (regular)
|82.09
|90.76
|77.75
|-
|PC (regular)
|90.91
|92.33
|90.20
|-
|SGL/BGL (upper bound; rough bin: GT)
|94.55
|95.37
|94.14
|-
|SGL/BGL (upper bound; rough bin: PC)
|91.79
|93.29
|91.04
|-
|SGL/BGL (learning) (rough bin: PC)
|N/A
|N/A
|89.94
|}

A metacode of a learning-based binarization method based on stroke gray level (SGL) and background gray level (BGL) is provided. The executable of the method will be provided in near future.

The proposed learning-based binarization method uses the SGL and the BGL to determine a locally-adaptive threshold value based on a parameter (alpha). The optimal selection of this parameter is the learning part of this method. 

=Related Dataset=
* [[Persian Heritage Image Binarization Dataset (PHIBD 2012)]]

=Related Ground Truth Data=
* [[Binarized images for PHIBD 2012 dataset]]

=References=
* [Ziaei2013] Hossein Ziaei Nafchi, Reza Farrahi Moghaddam, and Mohamed Cheriet. Persian historical document dataset with introduction to PhaseGT: A ground truthing application, to be submitted to ICDAR’13.
* [Ziaei2012] Hossein Ziaei Nafchi, Reza Farrahi Moghaddam and Mohamed Cheriet, Historical Document Binarization Based on Phase Information of Images, in ACCV’12 Workshop on e-Heritage, Daejeon, South Korea, Nov 5-10, 2012.
* [Farrahi2009] Reza Farrahi Moghaddam, and Mohamed Cheriet, RSLDI: Restoration of single-sided low-quality document images, Pattern Recognition, Volume 42, Issue 12, p.3355–3364 (2009) DOI: 10.1016/j.patcog.2008.10.021
* [Farrahi2010] Reza Farrahi Moghaddam, and Mohamed Cheriet, A multi-scale framework for adaptive binarization of degraded document images, Pattern Recognition, Volume 43, Issue 6, Number 6, p.2186–2198 (2010) DOI: 10.1016/j.patcog.2009.12.024
* [Cheriet2012] Mohamed Cheriet, Reza Farrahi Moghaddam, and Rachid Hedjam, A learning framework for the optimization and automation of document binarization methods, Computer Vision and Image Understanding, Volume Accepted, p.– (2012) DOI: 10.1016/j.cviu.2012.11.003

=Submitted Files=
==Version 1.0==
* [http://www.iapr-tc11.org/dataset/PHIBD2012/Training.txt  Train Set Meta Data] (0 Mb)
* [http://www.iapr-tc11.org/dataset/PHIBD2012/Test.txt   Test Set Meta Data] (0 Mb)
* [http://www.iapr-tc11.org/dataset/PHIBD2012/Otsu_PHIBD_2012.zip   baseline method] (0.39 Mb)
* [http://www.iapr-tc11.org/dataset/PHIBD2012/PC_PHIBD_2012.zip   baseline method] (0.33 Mb)
* [http://www.iapr-tc11.org/dataset/PHIBD2012/SGLBGL_PHIBD_2012.zip   baseline method] (0.36 Mb)
* [http://www.iapr-tc11.org/dataset/PHIBD2012/SGLBGL_PC_PHIBD_2012.zip   baseline method] (0.36 Mb)
* [http://www.iapr-tc11.org/dataset/PHIBD2012/SGLBGL_PC_TrainTest_PHIBD_2012.zip   baseline method] (0.36 Mb)
* [http://www.iapr-tc11.org/dataset/PHIBD2012/SGLBGL_metacode.m   Sample Program] (0 Mb)

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