Abstract
Handwriting signatures, as an important means of identity recognition, are widely used in multiple fields such as financial transactions, commercial contracts and personal affairs due to their legal effect and uniqueness. In forensic science appraisals, the analysis of offline handwriting signatures requires the appraiser to provide a certain number of signature samples, which are usually derived from various historical contracts or archival materials. However, the provided handwriting samples are often mixed with a large amount of interfering information, which brings severe challenges to handwriting identification work. This study proposes a signature handwriting denoising model based on the improved U-net structure, aiming to enhance the robustness of the signature recognition system. By introducing discrete wavelet transform and PCA transform, the model's ability to suppress noise has been enhanced. The experimental results show that this modelis significantly superior to the traditional methods in denoising effect, can effectively improve the clarity and readability of the signed images, and provide more reliable technical support for signature analysis and recognition.
Abstract (translated)
手写签名作为一种重要的身份识别手段，在金融交易、商业合同和个人事务等多个领域中广泛应用，因其法律效力和独特性而备受重视。在法医科学鉴定中，离线手写签名的分析要求评估人员提供一定数量的手写签名样本，这些样本通常从各种历史合同或档案材料中提取。然而，提供的书写样本往往掺杂了大量的干扰信息，这对手写识别工作带来了严峻挑战。本研究提出了一种基于改进U-net结构的手写签名去噪模型，旨在增强签名识别系统的鲁棒性。通过引入离散小波变换和主成分分析（PCA）变换，该模型的降噪能力得到了提升。实验结果显示，相比传统方法，此模型在去噪效果上具有显著优势，能够有效提高签名字迹的清晰度和可读性，并为签名分析与识别提供了更加可靠的技术支持。
URL
https://arxiv.org/abs/2507.00365
