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|Title:||Combined wavelet and gabor convolution neural networks||Authors:||Dagher, Issam
Abu Jamra, Samir
|Affiliations:||Department of Computer Engineering||Keywords:||CNN
|Issue Date:||2019||Part of:||International journal of wavelets multiresolution and information processing||Volume:||17||Issue:||6||Abstract:||
Handwriting recognition is a very active research in the machine learning community. In this paper, we tackled two important applications: handwritten digit recognition and Signature verification using convolution neural network (CNN). Signature is one of the most popular personal attributes for authentication. It is basic, shabby and adequate to individuals, official associations and courts. This paper focuses on offline signature verification (SV). It is a kind of a classification problem, which classifies the signature as genuine, or forgery. We use CNN in two types of datasets: the MNIST database, and UTSIG database. In order to obtain better accuracy, we propose to preprocess the data in the wavelet domain and in the Gabor filter combining the outputs of both CNN. This combination resulted in higher recognition accuracy compared to other techniques.
|Appears in Collections:||Department of Computer Engineering|
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