Statistical Analysis of Adaptive Thresholding Algorithms for Denoising Signature Images

This study explores the efficacy of adaptive thresholding techniques in denoising signature images captured under varying lighting conditions. Signature images from multiple individuals were obtained in different illumination scenarios, and three prominent adaptive thresholding algorithms, namely histogram thresholding, Otsu’s method, and the Gaussian Mixture Model (GMM), were applied to the noisy images. The performance of each technique was rigorously evaluated using root mean square error (RMSE) and correlation coefficient metrics. The findings reveal that the Gaussian Mixture Model significantly outperformed both histogram thresholding and Otsu’s method, achieving superior noise reduction and better preservation of essential information. This was evidenced by lower RMSE values and higher correlation coefficients. These results suggest that the Gaussian Mixture Model is a highly effective technique for denoising signature images, particularly under varying lighting conditions. Its superior performance underscores its potential as a robust tool for enhancing the clarity and accuracy of signature verification systems. This study provides valuable insights into the application of adaptive thresholding techniques in image processing, highlighting the advantages of the Gaussian Mixture Model over traditional methods. The implications of this research are substantial for fields that rely on precise signature recognition and verification, such as banking, legal documentation, and security systems. This study specifically focuses on signature segmentation as a preprocessing step for signature verification systems. It does not directly address full document verification but aims to improve segmentation accuracy under varying lighting conditions, which is a foundational component in document authentication pipelines.