An improved U-net model for offline handwriting signature denoising

Handwriting signatures, as an important means of identity recognition, are widely used in multiple fields 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, the model's ability to suppress noise has been enhanced. The experimental findings indicate that this model markedly outperforms conventional techniques in terms of noise reduction, thereby significantly enhancing the clarity and legibility of the signed images.