Handwriting Matching and Its Application to Handwriting Synthesis

Since it is extremely expensive to collect a large volume of handwriting samples, synthesized data are often used to enlarge the training set. We argue that, in order to generate good handwriting samples, a synthesis algorithm should learn the shape deformation characteristics of handwriting from real samples. In this paper, we present a point matching algorithm to learn the deformation, and apply it to handwriting synthesis. Preliminary experiments show the advantages of our approach.

• Type: Conference
• Year: 2005
• Where: ICDAR
• Authors: Yefeng Zheng, David S. Doermann
• Keywords: Document Analysis, ICDAR 2005, Point Matching Algorithm, Shape Deformation Characteristics, Synthesis Algorithm