A new approach for verifying off-line Persian signatures is presented, in this paper. In our method, feature extraction step is conducted based on estimated Fractal Dimension (FD) of signatures images, and making decision about acceptance/rejection of test signature is formulated as testing hypothesis which is used for the first time in order to verify offline Persian signatures. The proposed method has been tested on our new created database included 1000 genuine signatures and 200 skilled forgeries which have been collected from a population of 100 human subjects with different educational background. Obtained results confirm the effectiveness of the presented method.
A novel system for verifying signatures on Persian handwritten bank checks is presented, in this paper. The presented system includes two main phases called: training and verification phases. At first, the system is trained using some genuine signatures provided by each customer in training phase. Then verifying the signatures on incoming checks is carried out in the verification phase. Feature extraction step is conducted based on a new approach that uses Multitresolution box-counting (MRBC) method for estimating the fractal dimension of signatures. Here, signature verification is modeled as testing hypothesis, and decision about acceptance or rejection of signatures on incoming checks is carried out using Kolmogorov–Smirnov test. The presented system has been tested on two databases: our new created database and NISDCC database which was used for ICDAR 2009 signature verification competition. Our database has 1000 genuine signatures provided by 100 participants and 200 skilled forgeries copied from genuine samples by five forgers. In total our database includes 1200 Persian signatures. Obtained results show promising performance of the presented system for its application on Persian banks.
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