Writer Identification is a challenging problem based on small amount of handwritten text. It is
also a foremost research topic in forensic analysis of documents. For this, a convolution …

This paper reviews various text independent writer identification strategies using offline
documents. Various features extraction methodologies are discussed. Handcrafted and …

This paper presents an unsupervised approach for writer retrieval based on clustering SIFT
descriptors detected at keypoint locations resulting in pseudo-cluster labels. With those …

Usually, a writer identification system based on the convolutional neural network (CNN) is
designed as a closed system, which is composed of many convolutional layers trained often …

Automatic writer identification is a common problem in document analysis. State-of-the-art
methods typically focus on the feature extraction step with traditional or deep-learning-based …

Writer identification and writer retrieval deal with the analysis of handwritten documents
regarding the authorship and are used, for example, in forensic investigations. In this paper …

Writer Identification has gained increasing importance in the scientific community in recent
years. In this paper, we propose an approach based on the combination of local textural …

Writer retrieval has valuable applications in analyzing handwritten documents, such as the
verification of authenticity and authorship of unknown manuscripts. Writer retrieval systems …

This paper introduces SAGHOG, a self-supervised pretraining strategy for writer retrieval
using HOG features of the binarized input image. Our preprocessing involves the application …

Writer identification has practical applications for forgery detection and forensic science.
Most models based on deep neural networks extract features from character image or sub …