Handwritten signature verification system using hybrid transfer learning approach

The shortage of annotated images for handwritten signature verification continues to be a significant problem. However, making inferences from such a small amount of data is difficult. This article presents a novel approach for offline signature verification based on modified VGG19 transfer learning, which is a deep learning strategy to develop an unbiased model with high accuracy. The proposed model is validated with the data set BHSig260, which is in the Bengali language. The study used the pretrained model VGG-19 to extract features from each layer, followed by typical classification machine learning approaches. The suggested model has been validated using the various parameters, and it has a 97.8% accuracy with modified VGG19 and Random Forest. A comparison between the suggested method and the various current methods is also discussed in the study.