mlsanityData to 7 Oct 2026

Self-Supervised Learning Based Handwriting Verification

arXiv 2405.18320 ↗First posted 28 May 2024cs.CV cs.AI cs.CLcited by 1

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Abstract (arXiv)

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% accuracy, while ResNet-18 fine-tuned using Variance-Invariance-Covariance Regularization (VICReg) outperforms other contrastive approaches achieving 78% accuracy. Using a pre-trained VAE and VICReg for the downstream task of writer verification we observed a relative improvement in accuracy of 6.7% and 9% over ResNet-18 supervised baseline with 10% writer labels.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 43 of its 77 references that are in the map. First posted on arXiv 28 May 2024.

Builds on

43 of 77 references.

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Citing papers per year: 2024: 1.

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  1. 2024Vision-Language Model Based Handwriting Verificationcited by 0