@article{a65c81c5a9354cd5b6b7fca25dfb166a,
title = "Classification of document blocks using density feature and connectivity histogram",
abstract = "In this paper, we present a document block classification algorithm to automatically classify different types of blocks embedded in a document image. Two kinds of features, density feature and connectivity histogram, are devised to achieve the classification goal. In our approach, segmented document blocks are first classified into text and non-text blocks via the density feature. Then, the connectivity histogram is utilized to further classify non-text blocks into image and graphics blocks. Experimental results reveal the feasibility of the new technique in classifying document blocks.",
keywords = "Block classification, Connectivity histogram, Density feature",
author = "Fan, {Kuo Chin} and Wang, {Liang Shen}",
note = "Funding Information: Each document image contains many separate meaningful blocks within it. Hence it can be partitioned into smaller units of images, i.e., blocks. Each This work is supported by National Science Council of Taiwan under grant NSC-83-0408-E-008-001. * Corresponding author. Email: kcfan@ncuee.ncu.edu.tw of these smaller units belongs to some class which possesses certain homogeneous attributes that would be a good prior knowledge for classification. If a document image can be classified into different types of blocks by means of the homogeneous properties, we can further process them with other progressive steps, such as the recognition of text, vectoriZation of graphics, and compression of image.",
year = "1995",
month = sep,
doi = "10.1016/0167-8655(95)00039-J",
language = "???core.languages.en_GB???",
volume = "16",
pages = "955--962",
journal = "Pattern Recognition Letters",
issn = "0167-8655",
number = "9",
}