Digital access and transcription of historical and complex document images

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According to the United Nations Educational, Scientific and Cultural Organization (UNESCO), "Heritage is our legacy from the past, what we live with today, and what we pass on to the future generation." Since an international treaty was adopted by UNESCO in 1972, an ongoing effort to encourage the identification, protection, and preservation of outstanding heritage around the world has been under way. Particularly those focusing on visual cultural heritage, like ancient documents, have greatly benefited from signal processing (SP) and imaging advances. The task of making such documents legible and accessible is a daunting, but important one, especially when they have great historical significance. These documents may suffer from many forms of deterioration and physical degradation phenomena (background noise, artifacts caused by aging, interfering patterns, and the bleed-through or shadow-through of strokes), they may have been deliberately erased and overwritten, and there may be overlays. The objective of the proposed research program is to define a methodology for the realization of the principal components of a system designed to enhance the content of ancient documents and recover hidden information for the retrieval of digital images of those documents. The proposed methodology will devise a unified image analysis and classification framework. To achieve high-quality restoration, true physical image degradation modeling, which includes stochastic processes, will be developed to model the degradation phenomena. We will then be able to devise efficient reverse diffusion models capable of directly restoring these types of physical diffusion. To achieve high search capability, we will use incremental learning, putting experts (scholars and historians) in the loop to perform data classification and statistical topical class categorization in subspaces, which will reduce the search space and increase system efficiency. Our long-term goal is to build scalable intelligent systems with the human-like ability to seamlessly access and make use of the huge volume and variety of ancient and highly degraded multilingual documents that exists.