Document page segmentation using neuro-fuzzy approach
- 作者：Laura Caponetti ; Ciro Castiello ; Przemysł ; aw Gó ; recki
- 关键词：Document analysis ; Page segmentation ; Neuro-fuzzy classification ; Multiresolution ; Feature extraction
- 刊名：Applied Soft Computing Journal
- 出版年：2008
- 期刊代码：9_15684946
- 类别：cp
- 出版时间：January 2008
- 卷：8
- 期：1
- 页码：118-126
- 文件大小：1333 K
摘要
In this work, we propose a new document page segmentation method, capable of differentiating between text, graphics and background, using a neuro-fuzzy methodology. Our approach is based firstly on the analysis of a set of features extracted from the image, available at different resolution levels. An initial segmentation is obtained by classifying the pixels into coherent regions, which are successively refined by the analysis of their shape. The core of our approach relies on a neuro-fuzzy methodology, for performing the classification processes. The proposed strategy is capable of describing the physical structure of a page in an accurate way and proved to be robust against noise and page skew. Additionally, the knowledge-based neuro-fuzzy methodology allows us to understand the classification mechanisms better, contrary to what happens when other kinds of knowledge-free methods are applied.