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3 Journal of Medical Informatics & Technologies

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Offline signature verification using direction-based Shape Contexts

Adamski M., Saeed K.

Journal of Medical Informatics & Technologies

2014

Vol. 23

149--154

In this paper we present a system for offline signature verification using direction-based Shape Contexts. Images of handwritten signatures were thinned using KMM algorithm and then represented by a set of Shape Context descriptors computed separately in 4 directions in pixel’s 8-neighborhood. The distance measure used to compare Shape Contexts was based on L2 norm. The experiments were conducted using signatures from GPDS database.

Offline signature verification based on shape contexts using shared and user-specific thresholds

Adamski M., Saeed K.

Journal of Medical Informatics & Technologies

2013

Vol. 22

195--201

In this paper we present a system for offline signature verification based on Shape Context Descriptors. The system input are binarized images of handwritten signatures from GPDS database available for non-commercial research. During preprocessing each signature image is thinned using KMM algorithm in order to obtain 1-pixel wide skeleton. The feature vector is built from Shape Context Descriptors computed for selected points on skeletonized signature line. The verification process is based on the distance measure that uses Shape Context Descriptors. The presented system is evaluated using random and skilled forgeries with shared and user-specific thresholds.

Signature image recognition by shape context image matching

Adamski M., Saeed K.

Journal of Medical Informatics & Technologies

2007

Vol. 11

89--95

This paper presents experiments on recognition of signature images. In preprocessing stage a thinning algorithm is used followed by a sampling technique. Sampled points are used to calculate shape context histograms and based on their values corresponding pairs of points from reference and tested signature objects are selected. A distance measure based on shape contexts is used to classify analysed signatures.