We present a tool for re-ranking the results of a specific query by considering the (n+1) × (n+1) matrix of pairwise similarities among the elements of the set of n retrieved results and the query itself. The re-ranking thus makes use of the similarities between the various results and does not employ additional sources of information. The tool is based on graphical Bayesian models, which reinforce retrieved items strongly linked to other retrievals, and on repeated clustering to measure the stability of the obtained associations. The utility of the tool is demonstrated within the context of visual search of documents from the Cairo Genizah and for retrieval of paintings by the same artist and in the same style.

Many significant historical corpora contain leaves that are mixed up and no longer bound in their original state as multi-page documents. The reconstruction of old manuscripts from a mix of disjoint leaves can therefore be of paramount importance to historians and literary scholars. Previously, it was shown that visual similarity provides meaningful pair-wise similarities between handwritten leaves. Here, we go a step further and suggest a semiautomatic clustering tool that helps reconstruct the original documents. The proposed solution is based on a graphical model that makes inferences based on catalog information provided for each leaf as well as on the pairwise similarities of handwriting. Several novel active clustering techniques are explored, and the solution is applied to a significant part of the Cairo Genizah, where the problem of joining leaves remains unsolved even after a century of extensive study by hundreds of scholars.

The Digital Age has brought with it large-scale digitization of historical records. The modern scholar of history or of other disciplines is often faced today with hundreds of thousands of readily-available and potentially-relevant full or fragmentary documents, but without computer aids that would make it possible to find the sought-after needles in the proverbial haystack of online images. The problems are even more acute when documents are handwritten, since optical character recognition does not provide quality results. We consider two tools: (1) a handwriting matching tool that is used to join together fragments of the same scribe, and (2) a paleographic classification tool that matches a given document to a large set of paleographic samples. Both tools are carefully designed not only to provide a high level of accuracy, but also to provide a clean and concise justification of the inferred results. This last requirement engenders challenges, such as sparsity of the representation, for which existing solutions are inappropriate for document analysis.