Progress in the Application of Deep Learning in Forensic Scientific Document Examination

Forensic document examination, a pivotal branch of forensic science, involves the meticulous analysis and authentication of various document forms, including handwriting, printed materials, and seal impressions. With the relentless progression of technology, the integration of deep learning methodologies has significantly accelerated the automation and intelligence in this field. Specifically, the employment of complex multi-layered neural network models within deep learning has facilitated a heightened level of document image recognition and analysis, surpassing the capabilities of conventional approaches. This technological breakthrough has not only enhanced the accuracy and efficiency of forensic document examination but also substantially reduced the influence of human error and subjectivity, thereby bolstering the credibility of results. This article provides a thorough review of the contributions by domestic and international researchers in leveraging deep learning for different aspects of document examination. It delves into the advancements in handwriting analysis, which involves the identification and comparative assessment of individual writing styles; printed document verification, which focuses on the authenticity of printed materials; and seal impression inspection, where the authenticity and source of seal marks are scrutinized. The discussion includes an overview of the foundational principles underpinning these methodologies, the specific applications of deep learning in these areas, and the cutting-edge research findings propelling the field forward. In addition to highlighting these advancements, the article also critically examines the existing obstacles and constraints in applying deep learning to forensic document examination. These include the demand for more robust and generalizable models capable of accommodating the extensive variability encountered in real-world documents, the necessity of extensive and diverse datasets to train these models, and the challenges associated with integrating deep learning tools into established forensic workflows. The article concludes by offering insights into the future directions for research and application, emphasizing the potential for deep learning to further revolutionize forensic document examination as the technology continues to mature.