An Improved U-Net Model for Offline handwriting signature denoising

TL;DR

This paper introduces an improved U-Net based model incorporating wavelet and PCA transforms to effectively denoise offline handwriting signatures, enhancing their clarity for better recognition and forensic analysis.

Contribution

The study presents a novel U-Net based denoising model with wavelet and PCA transforms, significantly improving signature image clarity over traditional methods.

Findings

Superior denoising performance compared to traditional methods

Enhanced clarity and readability of signature images

Provides more reliable support for signature recognition

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.