Difference between revisions of "Persian Heritage Image Binarization Dataset (PHIBD 2012)"
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* [http://www.iapr-tc11.org/dataset/PHIBD2012/Original.zip Original images] (5 Mb)
Latest revision as of 00:30, 4 July 2013
Hossein Ziaie Nafchi, Seyed Morteza Ayatollahi, Reza Farrahi Moghaddam, and Mohamed Cheriet Synchromedia Laboratory ETS, Montreal, (Quebec) Canada H3C 1K3 E-mail: email@example.com Tel: +1(514)396-8972 Fax: +1(514)396-8595
Document Image Binarization, Persian Heritage, Handwritten manuscripts
This dataset contains 15 historical and old manuscript images collected from the historical
records at the Documents and old manuscripts treasury of Mirza Mohammad Kazemaini (affiliated
with Hazrate Emamzadeh Jafar), Yazd, Iran. The images suffer from various types of degradation
including bleed-through, faded ink, and blur. The dataset is the first in a series to provide
document images and their ground truth as a contribution to Document image analysis and
recognition (DAIR) community.
It is planned to increase the dataset in future and to create a dataset which also covers the tasks of understanding in the near future.
Metadata and Technical Details
As metadata, the types of degradation on each document image have been provided in two text
files: 1) for images number 1 to 5 and 2) for images number 6 to 15. It is worth noting that
images number 1 to 5 are considered as the training set while images number 6 to 15 are
considered as the test set for those binarization methods that are based on a learning technique.
Also, the estimated line height and stroke width for each image are provided in these files.
The original document images are 4.9MB, while their ground truth images are 324KB.
Ground Truth Data
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
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.
- [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historical document dataset with introduction to PhaseGT: A ground truthing application, to be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- [Ziaei2012] Hossein Ziaei Nafchi, Reza Farrahi Moghaddam and Mohamed Cheriet, Historical
Document Binarization Based on Phase Information of Images, in ACCV’12 Workshop on e-Heritage,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:
- [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
- Original images (5 Mb)
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