改进U-Net模型，有效去除手写签名图像噪声，提升识别可靠性。
An Improved U-Net Model for Offline handwriting signature denoising
- 结合离散小波与PCA变换增强去噪能力
- 在清晰度和可读性上显著优于传统方法
- 适合法医鉴定与电子签章系统应用
手写签名作为身份识别的重要手段，广泛应用于金融交易、商业合同和个人事务中。在司法鉴定中，需分析多份历史合同或档案中的签名样本，但这些原始图像常含大量干扰信息，严重阻碍识别工作。本文提出一种基于改进U-Net结构的签名去噪模型，引入离散小波变换与PCA变换，显著提升噪声抑制能力。实验结果表明，该模型在去噪效果上明显优于传统方法，能有效提高签名图像的清晰度与可读性，为签名分析与识别提供更可靠的技術支持。
原文摘要 · Abstract (English)
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.
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