Chinese Writer Identification Using Contour-Directional Feature and Character Pair Similarity Measurement
The key issue of Chinese writer identification is the uncertainty of the text content in the query and reference handwriting images. We propose a method for Chinese writer identification using Contour-directional Feature (CDF) and Character Pair Similarity Measurement (CPSM). CDFs are extracted from the query and reference handwriting images and are used to calculate the text-independent similarity between the query and reference handwriting images. Meanwhile, characters appearing in both the query and reference handwriting images are also utilized to measure the similarity of character pairs. The text-independent similarity and the similarity of character pairs are fused to the final similarity between the query and reference handwriting images. The proposed method is evaluated on two public datasets. The best Top-1 identification accuracy on the HIT-MW and CASIA-2.1 dataset reaches 96.7% and 97.9% respectively, which outperforms other previous approaches.