对比掩码自编码器用于字符级开放集笔迹鉴定
Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification
AI总结：
本文提出对比掩码自编码器（CMAE），结合掩码自编码与对比学习，用于字符级开放集笔迹鉴定，在CASIA数据集上达到89.7%的精确率，实现最先进性能。
摘要：
在数字取证和文档认证领域，笔迹鉴定在根据书写风格确定文档作者方面发挥着至关重要的作用。笔迹鉴定中的主要挑战是“开放集场景”，即目标是准确识别模型训练期间未出现过的书写者。为了克服这一挑战，表示学习是关键。该方法能够捕捉独特的笔迹特征，使其能够识别训练期间未曾遇到过的风格。基于这一概念，本文引入了对比掩码自编码器（CMAE）用于字符级开放集笔迹鉴定。我们将掩码自编码器（MAE）与对比学习（CL）相结合，以同时且分别地捕捉序列信息并区分多样的书写风格。为证明其有效性，我们的模型在CASIA在线笔迹数据集上达到了最先进（SOTA）的结果，达到了令人印象深刻的89.7%的精确率。我们的研究通过一种先进的表示学习方法推进了通用笔迹鉴定，为不断发展的数字笔迹分析领域做出了重大贡献，并满足了日益互联世界的需求。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
