<p>Technological advancements and image processing software have made document fraud more prevalent, aided by the availability of inexpensive scanners and printers that facilitate document alteration. Document forensics addresses this issue through active and passive techniques for detecting forgeries. Active methods, like using extrinsic fingerprints and signatures, help in straightforward document authentication. In contrast, passive methods require more sophisticated verification techniques. This review article explores various strategies for identifying the source printers of digital documents and the authors of scanned handwritten documents. It examines a range of methodologies, including machine learning and deep learning approaches, relevant to document forensics. Additionally, the article reviews research on detecting different types and models of printers using textual detection and noise analysis. It also discusses classification and feature extraction techniques, providing a comprehensive overview of the current state-of-the-art methods and tools in the field of document forensics.</p>