Identifying the Personality Traits Using Handwriting Recognition in a Real-Time Environment
Shalaka Prasad Deore, Pranjal Kalokhe, Sneha Patil, Sayali Nagarkar, Vidhi Oswal · Ingénierie des systèmes d information · 2025
Traditional methods of personality assessment, such as questionnaires and interviews, rely on self-reporting and subjective interpretation, which can be influenced by biases and social desirability.A handwriting-based approach offers an alternative method that provides nonverbal cues and unconscious expressions, supplementing traditional methods and potentially offering more objective insights, especially with an automated approach, into personality traits.Non-real-time methods take longer due to manual analysis, and are often subjective and prone to bias, while real-time analysis with a convolutional neural network (CNN) model provides instant results.Real-time tools would have no human intervention, are more convenient and easily accessible.Reducing human intervention through real-time analysis with a CNN model enhances the reliability, objectivity, scalability, and speed of the handwriting assessment process, providing a significant advantage over traditional methods.The proposed system focuses on the use of deep learning (DL) techniques to determine a person's personality by analyzing their handwriting.With the use of deep learning, human intervention is much less, thus the variance in the results is also much less which ultimately increases precision in the final predictions or results.Not only does the project predict personality traits through handwriting analysis, but also does that in a realtime environment.Steps such as preprocessing, feature extraction, and label classification are involved in the prediction process.CNN model has been used in the proposed system, for the final Personality Prediction.The CNN model for handwriting analysis must balance speed and accuracy through efficient architecture design and parameter optimization, minimizing computational complexity and inference time.To achieve this challenge two models (or versions) were built, the second having better data augmentation with respect to image sizing to be given to the CNN Network.Performance using the evaluation metrics was calculated with a testing efficiency of 0.71 in model 1 and 0.74 in model 2 and 82% accuracy was obtained on this real-time data.