Document Analysis Toolset

Inspiration

Document fraud costs organizations billions annually, with forged signatures and manipulated PDFs becoming increasingly sophisticated. Traditional manual verification methods are time-consuming, subjective, and often miss subtle digital alterations.

I was inspired by real-world cases where: - Legal contracts were invalidated due to undetected signature forgeries - Financial institutions lost money to document manipulation - Court cases hinged on proving document authenticity

My goal: democratize advanced document forensics using AI.

What it does

The Document Analysis Toolset is a comprehensive AI-powered platform that provides three core forensic capabilities with models trained using HP AI Studio on NVIDIA GPUs.

The system automatically detects and extracts signatures from PDF documents using YOLO-based object detection, processes multi-page documents in seconds, and allows users to download individual signature crops for further analysis.

For signature verification, the deep learning classifier compares two signature images to determine authenticity with confidence scoring, providing side-by-side visual comparisons and expert-level accuracy in distinguishing genuine signatures from forgeries. The manipulation detection module utilizes Error Level Analysis (ELA) to identify digitally altered areas in PDF documents, highlighting suspicious regions and tracking version changes to reveal tampering attempts that would be invisible to the naked eye.

The platform processes documents in real-time, supports multiple file formats, and presents results through clean, professional visualizations that make complex forensic analysis accessible to non-technical users.

How we built it

I built the system using a modern AI-first architecture. For the hosted web interface, I chose Streamlit for its rapid prototyping capabilities and clean presentation of complex data visualizations. The signature detection pipeline leverages a custom-trained YOLO model exported to ONNX format for optimized inference, while the forgery classification uses a fine-tuned Vision Transformer (ViT) model hosted on Hugging Face for the web versions, but both are also deployable for inference through HP AI Studio.

My development workflow utilized HP AI Studio for model experimentation and training, with an NVIDIA GPU (RTX 4070 Ti Super) providing the computational power needed for deep learning model development. I implemented MLFlow for experiment tracking and model versioning, ensuring reproducible results and systematic performance improvements. The manipulation detection module integrates PDF2Image for document conversion and PIL for advanced image processing operations.

Key technical decisions included using PyTorch with ONNX export for production deployment, implementing caching decorators for model loading optimization, and designing modular components that could be easily extended. I structured the codebase with separate pages for each analysis type, shared utility functions for common operations, and custom CSS styling for a professional user experience.

Challenges we ran into

One of the biggest challenges was optimizing model inference speed while maintaining accuracy. Initial implementations were too slow for real-time use, leading me to experiment with model quantization, ONNX optimization, and strategic caching. I also struggled with memory management when processing large PDF documents, ultimately implementing streaming approaches and efficient image handling.

The signature detection model required extensive data preprocessing and augmentation to handle the wide variety of signature styles, document qualities, and scanning artifacts found in real-world documents.

Accomplishments that we're proud of

I successfully created a production-ready document forensics platform that rivals expensive specialized software, making advanced AI accessible through a simple web interface. The signature detection achieves over 95% precision across diverse document types, while the forgery classification model demonstrates over 90% accuracy in distinguishing authentic from forged signatures.

The technical architecture is both scalable and maintainable, with clean separation of concerns and modular components that facilitate future development.

The Error Level Analysis implementation successfully identifies subtle digital manipulations that would be missed by manual inspection, providing forensic investigators with a powerful new tool for document authentication. The entire system processes documents in real-time, making it practical for high-volume use cases in professional environments.

Perhaps most importantly, this demonstrates that advanced AI capabilities can be democratized - bringing laboratory-grade document forensics to any professional who needs it, regardless of technical background or budget constraints.

What we learned

This project taught me valuable lessons about productionizing machine learning models and the importance of user-centered design in AI applications. I learned that model accuracy is only one piece of the puzzle - inference speed, memory efficiency, and result interpretability are equally crucial for real-world deployment.

What's next for Document Analysis Toolset

My immediate roadmap includes expanding the signature verification model to handle a broader range of signature styles and document types, implementing ensemble methods to combine multiple detection approaches for higher accuracy, and adding support for additional document formats beyond PDF.