Abstract: Handwritten character completion is an important research topic in image completion. Its challenge comes from the completion of the structural relationships in handwritten characters with unconstrained handwritten styles. To simulate the complicated and difficult situations in the real-world applications, the paper focuses on handwritten pictographic characters with large category, small sample size, multiple unconstrained handwritten styles, and unknown language (i.e., with no access to the class label of each character). Inspired by the progress in natural image completion, the generative adversarial network with global and local consistency was leveraged to achieve handwritten character completion. Under the circumstances of large category and various writing styles, the completion areas of character completion suffer from low-fidelity because of the large number of potential completion candidates. To solve this problem, a two-stage character completion system was proposed: the first stage is coarse-grained completion module ensuring the completeness of the character; the second stage is fine-grained completion module improving the sharpness and details of characters. Extensive experiments were conducted on CASIA-HWDB1.1 to validate the effectiveness of the two-stage system and analyze the completion performance under different writing styles and different conditions of missing area.

Key words: generative adversarial network, handwritten character, image completion, structure completion, unsupervised learning