AI / Machine Learning

Smart Parking & Traffic Management System (IoT)

The project follows a structured machine learning workflow. First, genuine and forged signature images are collected and organized into a dataset. Each image is preprocessed by converting it to grayscale, removing noise, resizing, and applying thresholding to improve quality.

Smart Parking & Traffic Management System (IoT)
AI / Machine Learning

Smart Parking & Traffic Management System (IoT)

AI Signature Verification System is an intelligent desktop/web application developed to automatically verify handwritten signatures using image processing and classical machine learning techniques. The system helps organizations authenticate signatures quickly, accurately, and securely, reducing manual verification time and minimizing human errors.

The application allows administrators to register users, upload genuine signature samples, train a machine learning model, and verify newly submitted signatures. During verification, the system preprocesses the signature image, extracts important visual features, and compares them with previously stored genuine signatures. The trained model then predicts whether the signature is genuine or forged and displays the confidence score along with the verification result.AI Signature Verification System is an intelligent desktop/web application developed to automatically verify handwritten signatures using image processing and classical machine learning techniques. The system helps organizations authenticate signatures quickly, accurately, and securely, reducing manual verification time and minimizing human errors.

The application allows administrators to register users, upload genuine signature samples, train a machine learning model, and verify newly submitted signatures. During verification, the system preprocesses the signature image, extracts important visual features, and compares them with previously stored genuine signatures. The trained model then predicts whether the signature is genuine or forged and displays the confidence score along with the verification result.

python,php · Completed 2026
The Approach

The project follows a structured machine learning workflow. First, genuine and forged signature images are collected and organized into a dataset. Each image is preprocessed by converting it to grayscale, removing noise, resizing, and applying thresholding to improve quality. Relevant image features are then extracted using image processing techniques. These features are used to train a classical machine learning model capable of distinguishing between genuine and forged signatures. Once training is completed, the trained model is integrated into a PHP-based web application through a Python API. During verification, the uploaded signature undergoes the same preprocessing and feature extraction steps before being classified by the trained model. The prediction result, confidence score, and verification status are stored in the MySQL database and displayed to the user through an intuitive interface.

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