Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification

TL;DR

This paper introduces CMAE, a novel approach combining Masked Auto-Encoders and Contrastive Learning for character-level open-set writer identification, achieving state-of-the-art results in recognizing unseen handwriting styles.

Contribution

It presents a new model that effectively captures handwriting features and distinguishes styles, advancing open-set writer identification techniques.

Findings

Achieves 89.7% precision on CASIA dataset

Outperforms previous state-of-the-art methods

Demonstrates robustness in recognizing unseen writers

Abstract

In the realm of digital forensics and document authentication, writer identification plays a crucial role in determining the authors of documents based on handwriting styles. The primary challenge in writer-id is the "open-set scenario", where the goal is accurately recognizing writers unseen during the model training. To overcome this challenge, representation learning is the key. This method can capture unique handwriting features, enabling it to recognize styles not previously encountered during training. Building on this concept, this paper introduces the Contrastive Masked Auto-Encoders (CMAE) for Character-level Open-Set Writer Identification. We merge Masked Auto-Encoders (MAE) with Contrastive Learning (CL) to simultaneously and respectively capture sequential information and distinguish diverse handwriting styles. Demonstrating its effectiveness, our model achieves state-of-the-art (SOTA) results on the CASIA online handwriting dataset, reaching an impressive precision rate of 89.7%. Our study advances universal writer-id with a sophisticated representation learning approach, contributing substantially to the ever-evolving landscape of digital handwriting analysis, and catering to the demands of an increasingly interconnected world.