Static and Dynamic Synthesis of Bengali and Devanagari Signatures
This work addresses the problem of data scarcity for signature verification in specific Indic scripts, though it is incremental as it adapts an existing method to new domains.
The paper tackled the challenge of generating synthetic signatures for Indic scripts (Bengali and Devanagari) to address the need for large training samples in signature verification systems, reporting promising results in appearance and performance compared to real signatures.
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.