Article: A novel signature recognition system using a convolutional neural network and fuzzy classifier Journal: International Journal of Computational Vision and Robotics (IJCVR) 2026 Vol.16 No.4 pp.427 - 438 Abstract: The present work provides a novel method for recognising the signature images, based on machine learning algorithms; convolutional neural network (CNN) and fuzzy min max classifier (FMMC). The new system goes through three phases; pre-processing, features extraction and classification. First of all, a variety of pre-processing techniques are used to isolate the signature pixels from the background. The resulting images are scanned with multiple filters to perform the convolution and ReLU procedure. The pooling process is then applied. Finally, the resulting image pixels are flattened and used to feed FMMC. Three systems containing the most used techniques including; profile projection-FMMC, Loci-FMMC and CNN; have been compared to the proposed system. The first two models are used to prioritise the feature extraction method of our system, while the third model, CNN, is utilised to prioritise the FMMC as classifier. The experimental results have obtained a good recognition rate equal to 97% which confirm the effectiveness of the proposed structure. Inderscience Publishers - linking academia, business and industry through research

Title: A novel signature recognition system using a convolutional neural network and fuzzy classifier

Authors: Ouafae El Melhaoui; Soukaina Benchaou; Redouan Zarrouk

Addresses: Electronic and System Laboratory, Faculty of Sciences (FSO), Mohammed First University, Oujda, 60000, Morocco; Research Center, High Studies of Engineering School, EHEI, Oujda, Morocco ' Laboratory MATSI, Faculty of Sciences, Mohammed First University, Oujda, 60000, Morocco ' Electronics and System Laboratory, Faculty of Sciences (FSO), Mohammed First University, Oujda, 60000, Morocco

Abstract: The present work provides a novel method for recognising the signature images, based on machine learning algorithms; convolutional neural network (CNN) and fuzzy min max classifier (FMMC). The new system goes through three phases; pre-processing, features extraction and classification. First of all, a variety of pre-processing techniques are used to isolate the signature pixels from the background. The resulting images are scanned with multiple filters to perform the convolution and ReLU procedure. The pooling process is then applied. Finally, the resulting image pixels are flattened and used to feed FMMC. Three systems containing the most used techniques including; profile projection-FMMC, Loci-FMMC and CNN; have been compared to the proposed system. The first two models are used to prioritise the feature extraction method of our system, while the third model, CNN, is utilised to prioritise the FMMC as classifier. The experimental results have obtained a good recognition rate equal to 97% which confirm the effectiveness of the proposed structure.

Keywords: convolutional neural network; CNN; fuzzy min max classification; FMMC; offline signature recognition.

DOI: 10.1504/IJCVR.2026.154151

International Journal of Computational Vision and Robotics, 2026 Vol.16 No.4, pp.427 - 438

Received: 09 Mar 2023
Accepted: 23 Mar 2024
Published online: 15 Jun 2026 *

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Title: A novel signature recognition system using a convolutional neural network and fuzzy classifier

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