Personality Analysis Using Graphology and Image Processing Techniques

This research studies the role of advanced image processing and graphology at a higher level of complexity, that is, predicting grief by interpreting handwriting. The aim of this study is to establish a reliable model, which gets a connection between the deep learning techniques and a certain set of handwriting parameters that would depict a person's mental state. This method combines the foundations of traditional graphology with the newest advancements in computer technology, such as TensorFlow and Keras, and use CNNs for feature extraction. Advanced machine learning methods including PCA, Random Forest, SVMs, and Gradient Boosting are used to improve the model's ability to classify emotional states from handwriting. The results show that the model has established discriminative capability in classification of 85% of samples with precision and recall values at 88% and 82%, respectively. This research amalgamating the traditional graphology with modern practices in psychology provides an opportunity for further enhancements in mental health evaluation through handwriting-based approaches.