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Multi-dilation Convolutional Neural Network for Automatic Handwritten Signature Verification

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Abstract

With the recent advancements in deep learning techniques, the application areas of unstructured data analytics are emerging in multiple domains. One of the popular applications is analyzing image unstructured data. Multiple image analytics-based solutions have been developed to establish an autonomous, cutting-edge computer-based approach for identification and verification. The exponential growth in text documents, images, and videos in every domain is driving the development of multiple image analytics-based applications to get insights and improve solutions. Secure authentication and verification of handwritten signatures play an important role in security and authentication, particularly in financial institutions, legal transactions, etc. One of the exciting applications of Deep Learning is automated signature verification for person identifications. Since signature verification is the most commonly accepted biometric attribute by law enforcement officials and agencies, making it more secure is a major challenging task. In the proposed work, the author developed an efficient multi-dilation convolutional neural network-based model for handwritten signature verification. It has been observed that the proposed model is memory efficient and does not require many pre-processing and hardware resources like GPU. The proposed model is validated on the CEDAR dataset which contains 24 genuine and 24 forgery off-line signatures for each of 55 writers. The authors have made a comparative analysis of the proposed method with other state-of-the-art methods discussed in the literature. The model achieves more than 99% accuracy with an equal error rate of 6.00 which is a good improvement over the other existing methods.

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Data availability

The datasets used in the current study are available from the corresponding author upon reasonable request. The link for the dataset is provided in the reference section.

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Authors and Affiliations

  1. Machine Vision and Intelligence Lab, Dept. of CSE, National Institute of Technology Jamshedpur, Jharkhand, 831014, India

    Rashmi Rathi Upadhyay, Ravishankar Mehta & Koushlendra Kumar Singh

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  1. Rashmi Rathi Upadhyay
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  2. Ravishankar Mehta
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  3. Koushlendra Kumar Singh
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Correspondence to Ravishankar Mehta.

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This article is part of the topical collection “Research Trends in Computational Intelligence” guest edited by Anshul Verma, Pradeepika Verma, Vivek Kumar Singh and S. Karthikeyan.

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Upadhyay, R.R., Mehta, R. & Singh, K.K. Multi-dilation Convolutional Neural Network for Automatic Handwritten Signature Verification. SN COMPUT. SCI. 4, 476 (2023). https://doi.org/10.1007/s42979-023-01931-w

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