Difference between revisions of "Persian Heritage Image Binarization Dataset (PHIBD 2012)"

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Revision as of 03:25, 31 May 2013

Datasets -> Datasets List -> Current Page

Created: 2013-05-30
Last updated: 2013-005-31

Contact Author

Hossein Ziaie Nafchi, Seyed Morteza Ayatollahi, Reza Farrahi Moghaddam, and Mohamed Cheriet
Synchromedia Laboratory
ETS, Montreal, (Quebec) Canada
H3C 1K3
E-mail: mohamed.cheriet@etsmtl.ca
Tel: +1(514)396-8972
Fax: +1(514)396-8595


Copyright

Current Version

1.0

Keywords

Document Image Binarization, Persian Heritage, Handwritten manuscripts

Description

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

Related Tasks

Software

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.

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historical document dataset with introduction to PhaseGT: A ground truthing application, to be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


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