HBPA: A Machine Learning Approach to Handwriting-Based Psychological Assessment

Overview

This project automates graphological analysis using machine learning to predict personality traits from handwriting. It processes handwritten images, extracts relevant features, and applies machine learning models to determine psychological attributes.

Table of Contents

• Installation
• Usage
• Dataset
• Features
• Results
• Future Scope
• Contributing
• License

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/handwriting-analysis.git

2. Navigate to the project directory:

   cd HBPA

3. Install the required packages:

   pip install -r requirements.txt

Usage

1. Ensure your handwriting samples are in the data/samples directory.
2. Run the analysis script:

   python train_predict_v3.py

3. The results will be displayed in the terminal.

Dataset

Data Acquisition

Data obtained from the IAM Handwriting Database of Research Group on Computer Vision and Artificial Intelligence, INF University of Bern, Switzerland.

• Contains 1538 pages of scanned text.

• 657 writers contributed samples of their handwriting.

• Each handwriting sample is labeled with corresponding psychological traits by manually studying each document.

• Dataset URL

  

  Sample data from IAM Handwriting Database

Image Processing

• Images are cropped and saved as PNG images with an automatic action script.

• The width of all the images is 850 pixels, and the height varies according to the content of the handwriting in the image.

  

  Data sample after image processing

Features

Pre-Processing

• Resolution enhancement
• Noise reduction
• Grayscale conversion
• Contour detection
• Warp affine transformation
• Horizontal and vertical projections

Feature Extraction

• Baseline
• Top margin
• Letter size
• Line spacing
• Word spacing
• Pen pressure
• Slant angle

Personality Traits

The system predicts eight key psychological attributes:

• Emotional Stability
• Mental Energy or Will Power
• Modesty
• Personal Harmony and Flexibility
• Lack of Discipline
• Poor Concentration
• Non-communicativeness
• Social Isolation

Results

Screenshots of actual terminal output of the model along with it's respective handwriting sample:

Output 1

output 2

Future Scope

Development of a User Interface (UI)

• Aim to design and implement an intuitive UI that facilitates easy access for the general public, enabling them to utilize the handwriting biometrics system effectively.

Integration of Convolutional Neural Networks (CNN)

• Transition from using Support Vector Machines (SVM) to employing CNNs for direct handwriting analysis, leveraging their advanced pattern recognition capabilities to enhance the system’s accuracy.

Optimization of Model Performance

• Focus on reducing the model’s execution time without compromising accuracy, ensuring a more efficient and responsive system for real-time applications.

Contributing

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-branch

3. Make your changes and commit them:

   git commit -m "Add new feature"

4. Push to the branch:

   git push origin feature-branch

5. Open a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.