Data-driven Enhancement of Chinese Calligraphy Aesthetic Style

Abstract

The generating of a large-scale Chinese character set from a small one has been notoriously challenging due to the complexity of character topology and the inherently elusive sophistication in glyphic aesthetics. This paper proposes an innovative approach to synthesize character-glyph, via which a large-scale character glyph set was generated modeling on a small-scale one of a desired style. The approach initiated from the sampling of designated calligraphic works and proceeded to build a character stroke database, followed by the proposal of F-histogram-based character topology. Drawing on aesthetic intuition, we abstract glyphic aesthetics to establish several evaluative rules, and we further designed an algorithm for the evaluation of the character topology with the Support Vector Machine (SVM) algorithm. At last, we adopted simulated annealing algorithm to optimize character glyphs with the desired style(s). Comparatively, this approach deserves credit in that the representation via F-histogram character topology accommodates more types of character topology and the synthesizing of glyphs integrates the stroke shape and the character topology.