Universal writing model for recovery of writing sequence of static handwriting images

Kai Kwong Lau*, Pong Chi YUEN, Yuan Yan Tang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Online features have been proven to be more robust information for handwriting recognition than an offline static image due to dynamic aspects, such as the writing sequence of strokes. The estimation of temporal information from a static image becomes an important issue. This paper presents a new statistical method to reconstruct the writing order of a handwritten signature from a two-dimensional static image. The reconstruction process consists of two phases, namely the training phase and the testing phase. In the training phase, the writing order with other attributes, such as length and direction, are extracted and analyzed from a set of training online handwritten signatures. A Universal Writing Model (UWM), which consists of a set of distribution functions, is then constructed. In the testing phase, the UWM is applied to reconstruct the writing order of an offline signature. 300 offline signatures with ground truth are used for evaluation. Experimental results show that about one-eighth of the reconstructed writing sequences are the same as the actual writing sequences.

Original languageEnglish
Pages (from-to)603-630
Number of pages28
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume19
Issue number5
DOIs
Publication statusPublished - Aug 2005

Scopus Subject Areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

User-Defined Keywords

  • Handwritten images
  • Signature verification
  • Universal writing model

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