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 language | English |
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Pages (from-to) | 603-630 |
Number of pages | 28 |
Journal | International Journal of Pattern Recognition and Artificial Intelligence |
Volume | 19 |
Issue number | 5 |
DOIs | |
Publication status | Published - Aug 2005 |
Scopus Subject Areas
- Software
- Computer Vision and Pattern Recognition
- Artificial Intelligence
User-Defined Keywords
- Handwritten images
- Signature verification
- Universal writing model