This review paper critically examines automated techniques for handwriting analysis in forensic science, focusing on their accuracy, efficiency, and limitations compared to traditional manual methods. Handwriting analysis has long been integral to forensic investigations, but manual approaches often face challenges such as subjectivity, scalability, and the inability to handle large datasets. The emergence of automated systems, powered by technologies like Optical Character Recognition (OCR), machine learning, and deep learning, has introduced trans formative capabilities, offering precision, scalability, and objectivity in handwriting analysis. Through a comparative analysis of automated techniques, including CEDAR-FOX, FLASH ID®, and advanced neural network models, this study highlights the strengths and weaknesses of these systems. While automated approaches demonstrate remarkable accuracy in feature extraction and scalability, they are limited by their dependency on high-quality input data, challenges in interpreting cursive or degraded handwriting, and the lack of standardized datasets. Moreover, ethical and legal concerns related to the black-box nature of AI systems and their admissibility in courtrooms are discussed. The paper concludes by recommending the integration of hybrid systems that combine human expertise with automation, the development of diverse training datasets, and the need for transparent algorithms. These advancements can bridge the existing gaps, ensuring reliable, efficient, and legally robust handwriting analysis in forensic applications.

Keywords: Automated Handwriting Analysis, Forensic Science, Machine Learning, Deep Learning, Feature Extraction, Handwriting Authentication