| Abstract
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The Archimedes Palimpsest is a manuscript containing the partial text of seven treatises by Archimedes that were copied onto parchment and bound in the tenth-century AD. This work is aimed at providing tools that allow scholars of ancient Greek mathematics to retrieve as much information as possible from images of the remaining degraded text. A correlation pattern recognition (CPR) system has been developed to recognize distorted versions of Greek characters in problematic regions of the palimpsest imagery, which have been obscured by damage from mold and fire, overtext, and natural aging. Feature vectors for each class of characters are constructed using a series of spatial correlation algorithms and corresponding performance metrics. Principal components analysis (PCA) is employed prior to classification to remove features corresponding to filtering schemes that performed poorly for the spatial characteristics of the selected region-of-interest. A probability is then assigned to each class, forming a character probability distribution based on relative distances from the class feature vectors to the ROI feature vector in principal component (PC) space. However, the current CPR system does not produce a single classification decision, as is common in most target detection problems, but instead has been designed to provide intermediate results that allow the user to apply his or her own decisions (or evidence) to arrive at a conclusion. To achieve this result, a probabilistic network has been incorporated into the recognition system. A probabilistic network represents a method for modeling the uncertainty in a system, and for this application, it allows information from the existing partial transcription and contextual knowledge from the user to be an integral part of the decision-making process. The CPR system was designed to provide a framework for future research in the area of spatial pattern recognition by accommodating a broad range of applications and the development of new filtering methods. For example, during preliminary testing, the CPR system was used to confirm the publication date of a fifteenth-century Hebrew colophon, and demonstrated success in the detection of registration markers in three-dimensional MRI breast imaging. In addition, a new correlation algorithm that exploits the benefits of linear discriminant analysis (LDA) and the inherent shift invariance of spatial correlation has been derived, implemented, and tested. Results show that this composite filtering method provides a high level of class discrimination while maintaining tolerance to within-class distortions. With the integration of this algorithm into the existing filter library, this work completes each stage of a cyclic workflow using the developed CPR system, and provides the necessary tools for continued experimentation.
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