Recovery of writing sequence of static images of handwriting using UWM

It is generally agreed that an on-line recognition system is always reliable than an off-line one. It is due to the availability of the dynamic information, especially the writing sequence of the strokes. This paper presents a new statistical method to reconstruct the writing order of a handwritten script from a two-dimensional static image. The reconstruction process consists of two phases, named the training phase and the testing phase. In the training phase, the writing order with other attributes, such as length and direction, are extracted from a set of training on-line handwritten scripts statistically to form a universal writing model (UWM). In the testing phase, UWM is applied to reconstruct the drawing order of offline handwritten scripts by finding the highest total probability. 300 off-line signatures with ground truth are used for evaluation. Experimental results show that the reconstructed writing sequence using UWM is close to the actual writing sequence.