SignVer is in Alpha and under active development. There may be significant changes ahead.
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