signature-recognition

A super lightweight image processing algorithm for detection and extraction of overlapped handwritten signatures on scanned documents using OpenCV and scikit-image.

The most powerful and customizable binary pattern scanner

Verify the authenticity of handwritten signatures through digital image processing and neural networks. ✍️

An end-to-end signature verification system to extract, clean and verify signatures in documents. Signatures are detected using YOLOv5. Noise is cleaned using a CycleGAN approach and verified. Keras / Tensorflow / PyTorch

Signature recognition is a behavioural biometric. It can be operated in two different ways: Static: In this mode, users write their signature on paper, digitize it through an optical scanner or a camera, and the biometric system recognizes the signature analyzing its shape. This group is also known as “off-line”. Dynamic: In this mode, users wri…

A package for signature detection

Developed a machine learning model for authenticating handwritten signatures. This project explores techniques for feature extraction and classification to verify signature authenticity.

Application to detect the similarity of two signatures.

Signature recognition with Keras,Deep learning

A large-scale offline Chinese handwritten signature dataset

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Tanda Tangan Digital (Android)

Signature Recognition with Siamese Network and CycleGAN

OpenCV Signature Detector

A python page to recognize the signature using CV2 library and back propagation algorithm

A web application for signature detection.

MultiScale Signature feature learning Network for offline handwritten signature verification

Research-Based Digital Signature Authentication: An Alternative to Password-Based Authentication with Integration Capabilities

Signature recognition using template matching and correlation

This repository is a machine learning–based system for offline handwritten signature verification that distinguishes genuine signatures from forgeries. It preprocesses signature images, extracts key visual features such as stroke patterns and shape, and trains a model to classify authenticity, offering an efficient solution for fraud detection.