Identifying the Personality Traits Using Handwriting Recognition in a Real-Time Environment
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".