Learning Visual Shape Lexicon for Document Image Content Recognition

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 -- 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 t...

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Guangyu Zhu, Xiaodong Yu, Yi Li, David S. Doermann
• Keywords: Complex Document Image, Computer Vision, Document Image Content, ECCV 2008, Google Image Search, Image Content Categorization, Image Content Recognition