@inproceedings{a0bb8387b9cf40e88620ca58422bb9be,
title = "A Coarse-to-Fine Approach for Layout Analysis of Ancient Manuscripts",
abstract = "Many applications along the manuscript analysis pipeline rely on the accuracy of pre-processing steps. Perfectly detecting the main text area in ancient historical documents is of great importance for these applications. We propose a learning-free approach to detect the main text area in ancient manuscripts. First, we coarsely segment the main text area by using a texture-based filter. Then, we refine the segmentation by formulating the problem as an energy minimization task and achieving the minimum using graph cuts. The energy function is derived from properties of the text components. Spatial coherence of the segmented text regions is explicitly encouraged by the energy function. We evaluate the suggested method on a publicly available dataset of 38 historical document images. Experiments show that the suggested approach outperforms another state-of-the-art page segmentation method in terms of segmentation quality and time performance.",
keywords = "Graph Cuts, Historical Documents, Layout Analysis, Page Segmentation, Statistical Inference",
author = "Abedelkadir Asi and Rafi Cohen and Klara Kedem and Jihad El-Sana and Itshak Dinstein",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 14th International Conference on Frontiers in Handwriting Recognition, ICFHR 2014 ; Conference date: 01-09-2014 Through 04-09-2014",
year = "2014",
month = dec,
day = "9",
doi = "10.1109/ICFHR.2014.31",
language = "English",
series = "Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "140--145",
booktitle = "Proceedings - 14th International Conference on Frontiers in Handwriting Recognition, ICFHR 2014",
address = "United States",
}