A new family of symbolic compression algorithms has recently been developed that includes the ongoing JBIG2 standardization effort as well as related commercial products. These techniques are specifically designed for binary document images. They cluster individual blobs in a document and store the sequence of occurrence of blobs and representative blob templates, hence the name symbolic compression. This paper describes a method for duplicate detection on symbolically compressed document images. It recognizes the text in an image by deciphering the sequence of occurrence of blobs in the compressed representation.
Photographs play a central role in many types of informal storytelling.
A portable form factor combined with a novel interface supports local sharing like a conventional photo album as well as recording of stories that can be sent to distant friends and relatives. Document image similarity and equivalence detection Jonathan J. Hull Pattern Recognition Letters, Special Issue on Image and Video Indexing, April 2001, pp. The detection of duplicate images is a useful means of indexing a large database of documents. An algorithm for duplicate document detection is proposed in this paper that operates directly on images that have been symbolically compressed using techniques related to the ongoing JBIG2 standardization effort. This paper describes a hidden Markov model method that recognizes the text in an image by deciphering data from the compressed representation.