Abstract
Signature recognition is an important requirement of automatic document verification system. Many approaches for signature recognition are presented in literature. A novel approach for off-line signature recognition system is presented in this paper, which is based on powerful wavelet features (maximum horizontal and vertical projection positions). The proposed system functions in three stages. Pre-processing stage; which consists of three steps: gray scale conversion, binarisation and fitting boundary box in order to make signatures ready for feature extraction, Feature extraction stage; where totally 64 wavelet based projection position features are extracted which are used to distinguish the different signatures. Finally in Neural Network stage; an efficient Learning Vector Quantization Neural Network (LVQ-NN) is designed and trained with 64 extracted features. The trained Neural Network is further used for signature recognition after the process of feature extraction. The average recognition accuracy obtained using this model ranges from 94 to 74 % with the training set of 15–50 persons.
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Angadi, S.A., Gour, S. (2013). LVQ-Neural Network Based Signature Recognition System Using Wavelet Features. In: S, M., Kumar, S. (eds) Proceedings of the Fourth International Conference on Signal and Image Processing 2012 (ICSIP 2012). Lecture Notes in Electrical Engineering, vol 222. Springer, India. https://doi.org/10.1007/978-81-322-1000-9_1
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DOI: https://doi.org/10.1007/978-81-322-1000-9_1
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