Signature Recognition

Language and Libraries

Problem Statement

Signature recognition involves building a system that can automatically distinguish between genuine signatures (signed by the actual person) and forged signatures (imitated by someone else). The system must work robustly in the presence of noise and natural variation in handwriting style.

Solution

A FastAPI-based web application that serves a ResNet-34 deep learning model fine-tuned for binary classification — genuine vs forged. The system supports two environments:

• Dev mode — runs locally, uses data from the data/ folder
• Prod mode — runs on GCP, pulls dataset from Google Cloud Storage

Dataset

CEDAR Signature Database — an offline signature verification dataset.

• 55 individuals, each with 24 genuine signatures → 1,320 genuine total
• 1,320 forged signatures (others imitating each person's signature)
• Scanned at 300 dpi grayscale, preprocessed with noise removal and slant normalization
• Folder structure: 001/ = genuine, 001_forg/ = forged

Model

ResNet-34 (pretrained on ImageNet, fine-tuned for binary classification)

• 34 layers: 33 convolutional + 1 fully connected
• Final FC layer replaced: outputs 2 classes (genuine / forged)
• Residual connections allow deeper training without degradation

Project Structure

├── app.py                      # FastAPI application (dev + prod)
├── main.py                     # Run training pipeline from CLI
├── config/config.yaml          # Dataset, training hyperparameters
├── .env                        # ENV_MODE=dev or prod
├── data/                       # Local dataset (dev only)
│   └── signature-recog/
│       ├── 001/                # Genuine signatures
│       ├── 001_forg/           # Forged signatures
│       └── ...
├── model/                      # Saved trained model
│   └── signature_model.pkl
├── templates/
│   └── index.html              # Web UI
├── src/
│   ├── components/
│   │   ├── data_ingestion.py   # Load from GCS (prod) or local (dev)
│   │   ├── data_transformation.py  # Dataset, transforms, DataLoaders
│   │   └── model_trainer.py    # ResNet-34 training loop
│   ├── pipeline/
│   │   └── training.py         # Orchestrates all pipeline steps
│   ├── entity/
│   │   ├── config_entity.py    # Config dataclasses
│   │   └── artifact_entity.py  # Artifact dataclasses
│   ├── configurations/
│   │   └── gcloud_syncer.py    # gsutil wrapper for GCS
│   ├── constants/__init__.py   # Paths, device, env mode
│   ├── utils/main_utils.py     # save/load object, YAML reader
│   ├── logger/__init__.py      # Timestamped file logging
│   └── exception/__init__.py   # Custom exception with line info
├── Dockerfile                  # Production container (GCP)
└── requirements.txt

API Endpoints

Method	Endpoint	Description
GET	/	Web UI with interactive feature cards
GET	/health	API status + model readiness check
POST	/train	Run full training pipeline
POST	/predict	Upload signature image → genuine / forged + confidence
GET	/docs	Swagger UI (dev mode only)

How to Run (Dev)

Step 1 — Clone the repository

git clone <repository-url>
cd signature-recognition-gcp

Step 2 — Create and activate virtual environment

python -m venv .venv
.\.venv\Scripts\Activate.ps1

Step 3 — Install dependencies

Windows users: install Microsoft Visual C++ Redistributable 2022 (x64) before installing torch.

pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
pip install -r requirements.txt

Step 4 — Add dataset

Place the CEDAR dataset inside the data/ folder:

data/
└── signature-recog/
    ├── 001/
    ├── 001_forg/
    └── ...

Step 5 — Configure environment

In .env, set:

ENV_MODE=dev

Step 6 — Run the server

python app.py

Open http://127.0.0.1:8000 in your browser. Click Train to train the model, then Predict to classify a signature image.

How to Run (Prod / GCP)

Step 1 — Configure environment

In .env, set:

ENV_MODE=prod

Make sure your GCS bucket (sig-recog) contains dataset.zip.

Step 2 — Authenticate GCloud

gcloud auth login
gcloud auth application-default login

Step 3 — Build and run Docker container

docker build -t signature-api .
docker run -d -p 8080:8080 signature-api

Open http://0.0.0.0:8080. Swagger docs are disabled in prod mode.

Tech Stack

Layer	Technology
Model	PyTorch, ResNet-34
API	FastAPI, Uvicorn
UI	Bootstrap 5, Vanilla JS
Storage	Google Cloud Storage
Container	Docker
Infrastructure	GCP (Cloud Run / Compute Engine)

Infrastructure Required (Prod)

1. Google Cloud Storage — dataset and model storage
2. Google Compute Engine or Cloud Run — server hosting
3. Google Artifact Registry — Docker image registry