Handwriting analysis and recognition have become crucial areas in biometrics, forensics, and document processing, with a wide range of applications including personality assessment, script identification, Handwriting recognition, keyword spotting, gender classification, writer identification, and signature verification. This presentation delves into the methodologies, challenges, and real-world implementations of these handwriting-based technologies. Traditional approaches rely on handcrafted features such as Gabor filters and Local Binary Patterns (LBP), while modern deep learning techniques, such as Convolutional Neural Networks (CNNs) and Siamese Networks, have significantly improved accuracy in tasks such as writer identification and offline signature verification. In addition, graphology-based personality assessment and gender classification utilize stylistic features such as stroke pressure, slant, and word spacing to infer behavioral traits. Despite these advancements, challenges still exist, including variability in handwriting styles, limited datasets (especially for historical scripts), and adversarial attacks in forgery detection. Future research directions focus on few-shot learning, explainable AI for graphology, and cross-script generalization. This comprehensive overview highlights the interdisciplinary nature of handwriting analysis, showcasing its potential in security, psychology, and digital archiving, while addressing open challenges and future trends in the field.