INTEGRATING COMPUTER VISION AND MACHINE LEARNING MODELS FOR HANDWRITING ANALYSIS

Handwriting has emerged as an essential characteristic in many applications, including forensic science, signature verification, and document authentication. Even though optimal character recognition (OCR) and machine learning (ML) have evolved more, the different handwriting styles combined with real-time processing issues have prevented current systems. For this reason, this study introduces a system that combines Computer Vision (CV) for preprocessing handwritten images with deep learning models CNN and LSTM for handwriting analysis. We believe integrating all these advanced techniques would improve handwriting analysis systems' accuracy and scalability. Moreover, our approach includes behavior capturing, which provides psychological insight based on the learned psychological cognition state of the writers, by using dynamic pen features such as pressure, writing speed, and stroke order. Compared to current state-of-the-art solutions, the proposed hybrid model has improved accuracy, real-time performance, and adaptability to different handwriting styles. This improves the accuracy of handwriting recognition and establishes a new avenue for behavioral profiling, potentially impacting fields like forensic investigation, psychological evaluation, and signature verification. Finally, the study concludes with prospects related to adding more behavioral traits and optimizing real-time processing capabilities.