# Static and Dynamic Synthesis of Bengali and Devanagari Signatures

arXiv: https://arxiv.org/abs/2401.17026 · HTML: https://mlsanity.com/paper/2401.17026/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 42 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Built on by (most cited first, 7 of 7)

- [A Perspective Analysis of Handwritten Signature Technology](https://mlsanity.com/paper/2405.13555/) (2024)
- [Neural network modelling of kinematic and dynamic features for signature verification](https://mlsanity.com/paper/2411.17506/) (2024)
- [Anthropomorphic Features for On-Line Signatures](https://mlsanity.com/paper/2501.09048/) (2025)
- [Design and Development of a Framework For Stroke-Based Handwritten Gujarati Font Generation](https://mlsanity.com/paper/2404.03277/) (2024)
- [Investigating the Common Authorship of Signatures by Off-Line Automatic Signature Verification Without the Use of Reference Signatures](https://mlsanity.com/paper/2405.14409/) (2024)
- [Online Signature Verification based on the Lagrange formulation with 2D and 3D robotic models](https://mlsanity.com/paper/2503.13573/) (2025)
- [Telling Human and Machine Handwriting Apart](https://mlsanity.com/paper/2601.11700/) (2026)

## Abstract (arXiv)

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.