TY - JOUR
T1 - Universal writing model for recovery of writing sequence of static handwriting images
AU - Lau, Kai Kwong
AU - Chi, Pong
AU - Tang, Yuan Yan
N1 - Funding Information:
This paper was supported by the Science Faculty of Hong Kong Baptist University.
PY - 2005/8
Y1 - 2005/8
N2 - 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.
AB - 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.
KW - Handwritten images
KW - Signature verification
KW - Universal writing model
UR - http://www.scopus.com/inward/record.url?scp=23944450610&partnerID=8YFLogxK
U2 - 10.1142/S0218001405004277
DO - 10.1142/S0218001405004277
M3 - Journal article
AN - SCOPUS:23944450610
SN - 0218-0014
VL - 19
SP - 603
EP - 630
JO - International Journal of Pattern Recognition and Artificial Intelligence
JF - International Journal of Pattern Recognition and Artificial Intelligence
IS - 5
ER -