Real Time Dynamic Threshold Clustering Method for Ancient Chinese Character Identification

Abstract

Character image clustering can find similar characters which have much reference value to ancient character identification. But the classical clustering method depends much on the threshold. To alter the threshold dynamically and get result in real time, a improved clustering method is proposed. The classical BIRCH CF tree was amended to chain hierarchical structure, and the new tree was built from bottom to top with KNN clustering method. Based on this structure, relative similarity degree was transformed to character number in clustering result. By converting the dynamic threshold clustering problem to finding the different cluster range, this method could get the clustering result in real time.