Boundary Gaussian Distance Loss Function for Enhancing Character Extraction from High-Resolution Scans of Ancient Metal-Type Printed Books

Abstract

This paper introduces a novel loss function, the boundary Gaussian distance loss, designed to enhance character segmentation in high-resolution scans of old metal-type printed documents. Despite various printing defects caused by low-quality printing technology in the 14th and 15th centuries, the proposed loss function allows the segmentation network to accurately extract character strokes that can be attributed to the typeface of the movable metal type used for printing. Our method calculates deviation between the boundary of predicted character strokes and the counterpart of the ground-truth strokes. Diverging from traditional Euclidean distance metrics, our approach determines the deviation indirectly utilizing boundary pixel-value difference over a Gaussian-smoothed version of the stroke boundary. This approach helps extract characters with smooth boundaries efficiently. Through experiments, it is confirmed that the proposed method not only smoothens stroke boundaries in character extraction, but also effectively eliminates noise and outliers, significantly improving the clarity and accuracy of the segmentation process.