Writer Retrieval

Writer retrieval aims to automatically identify documents written by the same person, a crucial task in analyzing historical archives and other large handwriting collections. Current research focuses on developing robust feature extraction methods, often employing Vision Transformers (ViTs) or Convolutional Neural Networks (CNNs), coupled with aggregation techniques like NetVLAD or its variants, and sometimes incorporating self-supervised learning to reduce reliance on labeled data. These advancements improve accuracy and efficiency in writer identification, impacting fields like historical document analysis and forensic science by automating a previously labor-intensive process. Character-level analysis and pre-training on synthetic data are also emerging as promising avenues for enhancing performance.