SignVer is an experimental library (not under active development) released as part of work on deep learning for offline signature verification. Read more here.
SignVer applies modern deep learning techniques in addressing the task of offline signature verification - given a pair (or pairs of) signatures, determine if they are produced by the same user (genuine signatures) or different users (potential forgeries). SignVer addresses this task by providing a set of modules that address subtasks required to implement signature verification in real world environments.
Returns a list of bounding boxes where signatures are located in an image.
from signver.detector import Detector
detector = Detector()
detector.load(detector_model_path)
boxes, scores, classes, detections = detector.detect(img_tensor)
plot_np_array(annotated_image, plot_title="Document and Extracted Signatures") 
Returns a list of cleaned signature images (removal of background lines and text), given a list of signature images
# Get image crops
signatures = get_image_crops(img_tensor, boxes, scores, threshold = 0.22 )
cleaned_sigs = cleaner.clean(np.array(signatures))
Returns a list of vector representations, given a list of image tensors/np arrays
from signver.extractor import MetricExtractor
extractor = MetricExtractor()
extractor.load(extractor_model_path)
features = extractor.extract(signature_list)Returns a distance measure given a pair of signatures
from signver.matcher import Matcher
matcher = Matcher()
matcher.cosine_distance(feat1,feat2) # 0.5
matcher.verify(feat1, feat2) # False