  Developing an automatic signature verification system is challenging and
demands a large number of training samples. This is why synthetic handwriting
generation is an emerging topic in document image analysis. Some handwriting
synthesizers use the motor equivalence model, the well-established hypothesis
from neuroscience, which analyses how a human being accomplishes movement.
Specifically, a motor equivalence model divides human actions into two steps:
1) the effector independent step at cognitive level and 2) the effector
dependent step at motor level. In fact, recent work reports the successful
application to Western scripts of a handwriting synthesizer, based on this
theory. This paper aims to adapt this scheme for the generation of synthetic
signatures in two Indic scripts, Bengali (Bangla), and Devanagari (Hindi). For
this purpose, we use two different online and offline databases for both
Bengali and Devanagari signatures. This paper reports an effective synthesizer
for static and dynamic signatures written in Devanagari or Bengali scripts. We
obtain promising results with artificially generated signatures in terms of
appearance and performance when we compare the results with those for real
signatures.
