Assessfy Pvt. Ltd Moderate 4 milestones 50 marks

Signature Verification System using Python

Real-world project · AICTE-aligned · AI-graded · Audit-ready certificate

Enroll as Student Mentor this project

Milestones

Available mentors

Enrolled students

Core skills

About this project

Signature Verification System using Python

Objective: Build a machine learning system to verify handwritten signatures for authentication purposes.

Context: Signature forgery remains a common fraud in India, affecting banking and legal processes; automated verification enhances security and trust.

What you'll build: You'll preprocess signature images with OpenCV, extract distinguishing features, and train a machine learning model using Scikit-learn and NumPy. The solution will include a simple GUI for uploading and verifying signatures in real time.

Deliverables: Provide the trained verification model, feature extraction scripts, and an integrated GUI application for signature authentication.

Milestones

1. Data preprocessing

10 marks 7d

Preprocess the provided signature image dataset by resizing images to a consistent dimension, converting them to grayscale, and normalizing pixel values. Submit a Python script and a sample of preprocessed images. 'Done' means your script runs without errors and outputs images ready for feature extraction, with clear documentation of each preprocessing step.

2. Feature extraction from signature images

10 marks 7d

Extract relevant features from the preprocessed signature images using techniques such as edge detection, contour analysis, or pixel intensity histograms. Submit your feature extraction code and a CSV file containing feature vectors for all images. 'Done' means your features are numerically represented, reproducible, and suitable for input to a machine learning model.

3. ML model training and testing

15 marks 7d

Train and test a machine learning model (e.g., SVM, Random Forest) using the extracted features to classify signatures as genuine or forged. Submit your training/testing code, model evaluation metrics (accuracy, precision, recall), and a brief summary of results. 'Done' means your model achieves reasonable performance and you clearly report and interpret the results.

4. GUI integration

15 marks 9d

Integrate your trained model into a simple Python GUI (e.g., Tkinter) that allows users to upload a signature image and receive a verification result. Submit the complete GUI code and a short demo video or screenshots showing the system in action. 'Done' means the GUI runs smoothly, accepts user input, and displays accurate verification outcomes.

Open internships using this project -->

Upcoming sessions

Session	Window	Enrolled
Signature Verification System using Python	11 Jun 2026 to 10 Jun 2028	0

Skills you'll learn

Machine LearningImage ProcessingFeature Extraction

Tools used

Python OpenCV Scikit-learn NumPy

Prerequisites

Python basics OpenCVScikit-learn

Available mentors

Priyang Kumar

Free

You'll earn — Certificate (PDF)

AICTE-aligned Project Completion Certificate

A formal, audit-ready PDF certificate issued by Assessfy + your institute on successful completion. Includes AICTE credit hours, your evaluator's signature, and a QR code for third-party verification.

AICTE-aligned

Certificate of Project Completion

This is to certify that

has successfully completed the project

Signature Verification System using Python

Scan to Verify

Date

Signature

Auto-issued on completion QR-verifiable

You'll earn — Digital Badge

Shareable LinkedIn / Resume Skill Badge

A compact, verifiable Open-Badges-2.0-compliant digital credential. Add to your LinkedIn profile, GitHub README, or resume in one click. Recruiters can validate authenticity via a unique URL.

Moderate

Signature Verification System using P…

Assessfy

Auto-issued on completion One-click LinkedIn add