Stroke parameters identification algorithm in handwriting movements analysis by synthesis.
This paper presents a new approach to identify the stroke parameters in handwriting movement data understanding. A two-step analysis by synthesis paradigm is employed to facilitate the coarse-to-fine parameter identification for all strokes. One is the stroke data extraction, the other is the coarse-to-fine stroke parameter identification. The new consideration of using this two-step paradigm is that the nonnegative primitive factorization technique is incorporated to decouple the overlapped strokes from the measurement data. In comparison to the existing paradigms of using the heuristic stroke data decoupling techniques, our paradigm presented here contributes to alleviating the difficulty of local optimum traps with the well-shaped initializations in the global optimization for jointly identifying stroke parameters. Moreover, our paradigm excludes the iteration between two steps, which contributes to the enhancement of computational efficiency. Experimental results are reported to validate the proposed approach.
Min
M
Liu
Xuemei
X
Guo
Guoli
G
Wang
D000465:Algorithms; D001185:Artificial Intelligence; D001699:Biometry / Q000379:methods; D006225:Hand / Q000502:physiology*; D006236:Handwriting; D006801:Humans; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013647:Task Performance and Analysis