@conference {13581,
	title = {Learning Visual Shape Lexicon for Document Image Content Recognition},
	booktitle = {The 10th European Conference on Computer Vision (ECCV 2008)},
	year = {2008},
	month = {2008///},
	pages = {745 - 758},
	address = {Marseille, France},
	abstract = {Developing effective content recognition methods for diverse imagery continues to challenge computer vision researchers. We present a new approach for document image content categorization using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant shape feature that is generic enough to be detected repeatably and segmentation free. We learn a concise, structurally indexed shape lexicon from training by clustering and partitioning feature types through graph cuts. We demonstrate our approach on two challenging document image content recognition problems: 1) The classification of 4,500 Web images crawled from Google Image Search into three content categories {\textemdash} pure image, image with text, and document image, and 2) Language identification of 8 languages (Arabic, Chinese, English, Hindi, Japanese, Korean, Russian, and Thai) on a 1,512 complex document image database composed of mixed machine printed text and handwriting. Our approach is capable to handle high intra-class variability and shows results that exceed other state-of-the-art approaches, allowing it to be used as a content recognizer in image indexing and retrieval systems.},
	author = {Zhu,Guangyu and Yu,Xiaodong and Li,Yi and David Doermann}
}
