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signature-recognition
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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 write their signature in a digitizing tablet, which acquires the signature in real time. Another possibility is the acquisition by means of stylus-operated PDAs. Some systems also operate on smart-phones or tablets with a capacitive screen, where users can sign using a finger or an appropriate pen. Dynamic recognition is also known as “on-line”. Dynamic information usually consists of the following information:
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
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.
Signature recognition using template matching and correlation
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…
Machine Learning model made by me and my colleagues for Signature and Facial Recognition which is helpful in Finance Sector considering the need of Security.
Research-Based Digital Signature Authentication: An Alternative to Password-Based Authentication with Integration Capabilities