Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Document Image Retrieval through Word Shape Coding
IEEE Transactions on Pattern Analysis and Machine Intelligence
High accuracy and language independent document retrieval with a fast invariant transform
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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Document analysis often starts with robust signatures, for instance for document lookup from low-quality photographs, or similarity analysis between scanned books. Signatures based on OCR typically work well, but require good quality OCR, which is not always available and can be very costly. In this paper we describe a novel scheme for extracting discrete signatures from document images. It operates on points that describe the position of words, typically the centroid. Each point is extracted using one of several techniques and assigned a signature based on its relation to the nearest neighbors. We will discuss the benefits of this approach, and demonstrate its application to multiple problems including fast image similarity calculation and document lookup.