This paper introduces a novel computational method for the recognition and grouping of characters printed with ancient movable metal types from Korea. Traditional comparative analysis methods struggle to consistently distinguish between typeface variations in digitized images of historical texts. Our approach addresses this limitation through a three-step process: skeletonization to extract structural features, image registration to align, and similarity metric computation to assess typeface matches. We evaluated various pre-processing techniques and metrics, identifying the Dice coefficient as the most reliable for character discrimination. The proposed algorithm combines binarization, skeletonization, iterative closest point registration, and distance measurement. Compared to conventional methods, our approach improves character grouping accuracy while reducing computation time by a factor of up to 45. This enhanced methodology allows for a more accurate reconstruction of the scale and variety of metal types produced, offering new insights into historical printing technologies and cultural developments.
Ahmed et al. (Sat,) studied this question.