AI-Driven Graphology Analysis System
End-to-end computer vision pipeline for handwriting-based personality prediction with real-time API inference
Project Summary
Developed an AI-powered handwriting analysis system that predicts personality traits from handwritten input using computer vision and machine learning, enabling automated, scalable, and consistent graphological assessment.
Problem Statement
- Traditional graphology relies heavily on subjective human interpretation with low consistency
- Manual handwriting analysis is time-consuming and not scalable for real-world applications
- Lack of standardized datasets and feature extraction pipelines for handwriting traits
- Difficulty integrating handwriting analysis into modern digital platforms (mobile/web)
System Architecture
[Architecture Diagram Placeholder]
The system follows a modular microservice-oriented architecture where a Next.js frontend handles user interaction and image upload, a FastAPI backend orchestrates requests, and a dedicated AI module processes handwriting images. The AI module leverages OpenCV for preprocessing, Tesseract for text extraction (if needed), and custom feature extraction pipelines for graphological traits. Processed features are passed into ML models for personality prediction. MongoDB is used for metadata storage, while image assets are stored on VPS disk storage. The system is containerized using Docker and deployed on a VPS with Traefik as a reverse proxy.
Model & Approach
- Designed a computer vision pipeline for handwriting preprocessing (grayscale conversion, noise reduction, contour detection)
- Extracted graphology-relevant features such as slant, spacing, baseline alignment, letter size, and pressure estimation
- Developed ML models to map extracted features to personality trait predictions
- Implemented modular AI service separation to allow independent scaling and updates
- Integrated OCR (Tesseract) selectively for text-aware feature enhancement
MLOps & Deployment
- Containerized AI, backend, and frontend services using Docker for reproducible deployment
- Designed modular pipeline enabling independent updates of CV models without affecting backend APIs
- Implemented logging and monitoring for inference outputs and error tracking
- Version-controlled models and preprocessing pipelines for reproducibility
- Planned CI/CD workflow for automated deployment and future retraining integration
Results & Impact
- Successfully automated handwriting feature extraction and personality prediction pipeline
- Reduced manual analysis effort from hours to seconds per sample
- Enabled scalable deployment via VPS with containerized services
- Built foundation for real-time inference through API-based architecture
- Achieved consistent and repeatable personality predictions compared to subjective manual analysis
Lessons Learned
- Feature engineering in handwriting analysis is more critical than model complexity
- Image preprocessing quality directly impacts downstream prediction accuracy
- Separation of AI module from backend improves maintainability and scalability
- Dataset quality and labeling consistency are major bottlenecks in graphology-based ML systems
- Early consideration of deployment (Docker, VPS, routing) simplifies transition from research to production