Layout Analysis for Arabic Historical Document Images Using Machine Learning

• Published in: 2012 International Conference on Frontiers in Handwriting Recognition pp. 639 - 644
• Main Authors: Bukhari, S. S., Breuel, T. M., Asi, A., El-Sana, J.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2012
• Subjects: Accuracy; Context; Feature extraction; historical manuscripts; Image segmentation; Layout; layout analysis; machine learning; Shape; Training
• ISBN: 9781467322621; 1467322628
• DOI: 10.1109/ICFHR.2012.227

Page layout analysis is a fundamental step of any document image understanding system. We introduce an approach that segments text appearing in page margins (a.k.a side-notes text) from manuscripts with complex layout format. Simple and discriminative features are extracted in a connected-component level and subsequently robust feature vectors are generated. Multilayer perception classifier is exploited to classify connected components to the relevant class of text. A voting scheme is then applied to refine the resulting segmentation and produce the final classification. In contrast to state-of-the-art segmentation approaches, this method is independent of block segmentation, as well as pixel level analysis. The proposed method has been trained and tested on a dataset that contains a variety of complex side-notes layout formats, achieving a segmentation accuracy of about 95%.