Offline Arabic signature verification (OSV) is a challenging biometric task due to the high stylistic variability of Arabic handwriting and the presence of skilled forgeries. This work proposes a hybrid verification system that integrates geometric feature extraction with a Takagi-Sugeno fuzzy inference model. After preprocessing, the system extracts the skeleton of each signature and detects key control points using the Shi-Tomasi algorithm. Four discriminative local geometric features-distance deviation, angular deviation, proportional distance ratio, and centroid deviation-are computed between matched control‑point pairs of the reference and test signatures. These features capture subtle structural inconsistencies introduced by genuine handwriting variation or forgery attempts. A fuzzy inference system with sixteen rules maps these features into a similarity score, and a writer‑dependent thresholding mechanism determines acceptance or rejection. Experiments conducted on a dataset of 50 Arabic writers demonstrate that the proposed method achieves competitive accuracy, reduces false acceptance and rejection rates, and provides an interpretable framework suitable for forensic and banking applications.
Al-Zubaidi et al. (Sat,) studied this question.