VAE & Simple GAN for Signature Generation

Generating realistic synthetic signatures with a VAE and a GAN.

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Domain
Generative AI
Year
2024
Type
Research report — internal engineering work

This is internal engineering and R&D work, not a client project. It is published to show technical depth — the modelling, evaluation, and tooling behind the AI automation we build for clients. No client is associated with it.

An implementation of a Variational Autoencoder (VAE) and a Generative Adversarial Network (GAN) to generate realistic fake signatures. The VAE learns latent representations by encoding and reconstructing signatures; the GAN's generator creates new signatures from noise while a discriminator judges realism.

Tools & methods

  • PyTorch
  • VAE (encoder/decoder)
  • GAN (generator/discriminator)
Full technical report

Objectives

  • Learn meaningful latent representations of signatures with a VAE.
  • Generate convincing synthetic signatures with a GAN.
  • Improve robustness on a limited dataset via augmentation.

Process

  • Augment the dataset (rotation, horizontal flip, Gaussian noise) for diversity.
  • Train the VAE with combined reconstruction + KL-divergence loss.
  • Train the GAN with binary cross-entropy adversarial loss.
  • Evaluate via reconstruction loss and qualitative comparison to real signatures.

Results

  • VAE achieved low reconstruction loss; GAN produced high-quality, realistic signatures.
  • Generated samples retained distinctive features of the originals.

What we took from it

  • VAE structural understanding + GAN realism are complementary.
  • Augmentation meaningfully enriches small training sets.

From the report

  • Generated signature samples (1)
    Generated signature samples (1)
  • Generated signature samples (2)
    Generated signature samples (2)
  • Generated signature samples (3)
    Generated signature samples (3)
  • Generated signature samples (4)
    Generated signature samples (4)
  • Generated signature samples (5)
    Generated signature samples (5)

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Every project and study. Showing 26 of 27.

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    • React.js
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    Delivered
    4 services
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    October 2024 – January 2025
    • Next.js
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    Platform live and operating nationwide at beyutlibya.com.

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    3 services
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    Live jewellery ERP available at ophir.eaquirstech.com.

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    Client engagement

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    Turner 10 Real Estate ERP

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    3 services
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    Live product
    • NestJS
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    Live real-estate ERP available at turner10.eaquirstech.com.

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    Independent product

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Engineering & AI R&D

This is internal and academic engineering work. Each study includes its full technical report.

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