EAGER: Large Scale Document Image Triage, Indexing and Retrieval

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Structural similarity search and retrieval in images that include both printed text and handwritten text remains a challenging problem, especially with collections that are noisy, and heterogeneous. Approaches currently in use generally convert documents before filtering. This work provides triage as a way to filter very large collections through structural similarity with known attributes, then new clustering with broader terms and hashing to extend the scale of collections considered. The work will provide new directions for document image retrieval, especially in conditions where there is a wide variation in structure and layout and will be made scalable in cloud environments. Another approach to scaling, especially in the area of duplicate detection, will extend multi-level locality sensitive hashing and generalize it to other analysis indexing and retrieval issues. In addition to including graduate students, results and software will be made available through Creative Commons licensing to provide for replication and extension of the results.