VerSign: Easy Signature Verification in Python
versign is a small Python package which can be used to perform verification of offline signatures.
It assumes no prior knowledge of any machine learning tools or machine learning itself, and therefore can be used by ML experts and anyone else who wants to quickly integrate this functionality into their project.
Getting Started
Installation
This package requires python 3. Installation can be done with pip:
pip install versign
You might also need to manually install the following dependencies:
pip install git+git://github.com/luizgh/visdom_logger#egg=visdom_logger
pip install git+https://github.com/luizgh/sigver.git
Installation inside a virtual environment is recommended.
Download Trained Models
Before you can get started with, there is one more step you need to complete. versign comes with some pre-trained models which give it its magic.
Download the compressed models here, and extract them to models/ directory in your project root. Your project directory should look something like this:
_ $PROJECT_ROOT
|__ models/
| |__ signet.pth
| |__ versign_segment.pkl
|__ ...
Organise Your Dataset
This model treats signature verification as a single-class learning problem where only positive samples (i.e. genuine signatures) are available during training. This is because, in real-world situations where we want to enrol users into a signature verification system for verifying their signatures later, we don't have any forgeries available unless we specifically obtain them. Which is not practical. However, both genuine and forged signatures can be present during testing.
Write Your First Program with VerSign
import os
from versign import VerSign
# Load training data
train_data # folder containing training data (only genuine samples)
x_train = [os.path.join(train_data, f) for f in sorted(os.listdir(train_data))]
# Load test data and labels
test_data # folder containing test data
x_test = [os.path.join(test_data, f) for f in sorted(os.listdir(test_data))]
# Train a writer-dependent model from training data
v = VerSign('models/signet.pth', (150, 220))
v.fit(x_train)
# Predict labels of test data
y_pred = v.predict(x_test)
For a more complete example and additional features such as measuring test accuracy if groundtruth is known, see example.py.
3 Aug 6, 2022
12 Jan 2, 2023
4 Mar 28, 2022
5 Oct 21, 2022
30 Sep 28, 2022
12 Dec 12, 2022
11 May 22, 2022
2 Dec 5, 2021
4.1k Dec 30, 2022
1 Nov 02, 2021
6k Jan 04, 2023
42 Jul 20, 2022
4 Mar 07, 2022
4 Sep 24, 2021
7 Dec 13, 2021
1 Nov 14, 2021
1 Nov 10, 2021
120 Dec 05, 2022
37 Jan 07, 2023
1 Nov 21, 2022
564 Dec 28, 2022
132 Dec 14, 2022
10 Oct 29, 2022
2 Dec 26, 2021
3 Nov 23, 2022
3 Nov 25, 2022
3 Aug 10, 2022