Header Based Classification of Journals Using Document Image Segmentation and Extreme Learning Machine

摘要

Document image segmentation plays an important role in classification of journals, magazines, newspaper, etc., It is a process of splitting the document into distinct regions. Document layout analysis is a key process of identifying and categorizing the regions of interest in the scanned image of a text document. A reading system requires the segmentation of text zones from non- textual ones and the arrangement in their correct reading order. Detection and labelling of text zones play different logical roles inside the document such as titles, captions, footnotes, etc. This research work proposes a new approach to segment the document and classify the journals based on the header block. Documents are collected from different journals and used as input image. The image is segmented into blocks like heading, header, author name and footer using Particle Swarm optimization algorithm and features are extracted from header block using Gray Level Co-occurrences Matrix. Extreme Learning Machine has been used for classification based on the header blocks and obtained 82.3% accuracy.