Off-line Handwritten Signature Recognition Using Wavelet Neural Network

Automatic signature verification is a well-established and an active area for research with numerous applications such as bank check verification, ATM access, etc. Most off-Line signature verification systems depend on pixels intensity in feature extraction process which is sensitive to noise and any scale or rotation process on signature image. This paper proposes an off-line handwritten signature recognition system using Discrete Wavelet Transform as feature extraction technique to extract wavelet energy values from signature image without any dependency of image pixels intensity. Since Discrete Wavelet Transform suffers from down-sample process, Wavelet Neural Network is used as a classifier to solve this problem. A comparative study will be illustrated between the proposed combination system and pervious off-line handwritten signature recognition systems. Conclusions will be appeared and future work is proposed.