Self-Supervised Learning Based Handwriting Verification
CoRR(2024)
Abstract
We present SSL-HV: Self-Supervised Learning approaches applied to the task of
Handwriting Verification. This task involves determining whether a given pair
of handwritten images originate from the same or different writer distribution.
We have compared the performance of multiple generative, contrastive SSL
approaches against handcrafted feature extractors and supervised learning on
CEDAR AND dataset. We show that ResNet based Variational Auto-Encoder (VAE)
outperforms other generative approaches achieving 76.3
ResNet-18 fine-tuned using Variance-Invariance-Covariance Regularization
(VICReg) outperforms other contrastive approaches achieving 78
a pre-trained VAE and VICReg for the downstream task of writer verification we
observed a relative improvement in accuracy of 6.7
supervised baseline with 10
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