Persian Heritage Image Binarization Dataset (PHIBD 2012) (PHIBD 2012)
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.
- [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 Persian historical document dataset with introduction to PhaseGT: A ground truthing application, to be submitted to ICDAR’13.
- [Ziaei2012] Hossein Ziaei Nafchi, Reza Farrahi Moghaddam and Mohamed Cheriet, Historical Document Binarization Based on Phase Information of Images, in ACCV’12 Workshop on e-Heritage, Daejeon, South Korea, Nov 5-10, 2012.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
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.
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.
|Original.zip||data||(5 MB)||21||Original images|
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