Geometrical and Structural Features for Forensics in Handwritten Bank Cheques

Wadhwa, Anupam; Maheshwari, Mohak; Dansena, Prabhat; Bag, Soumen

doi:10.1109/indicon45594.2018.8987116

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…Holi et al [53] proposed using CNN for identifying handwritten digits in bank checks. Wadhwa et al [54] proposed using geometrical and structural features, like stroke width and direction, with the radial-basis SVM and random forest to verify the style of writing on bank checks. Neelima et al [55] used the CNN for number recognition on checks, and Ghosh et al [56] proposed a method for identifying handwritten characters on checks issued by banks in India using the support vector machinebased classification of texture through the histogram of oriented gradients and the grey level co-occurrence matrix.…”

Section: Autonomous Check Processing and Verification Using Add-kycmentioning

confidence: 99%

Cyber-Physical Customer Management for Internet of Robotic Things-Enabled Banking

2023

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In-person banking is still an important part of financial services around the world. Hybrid bank branches with service robots can improve efficiency and reduce operating costs. An efficient autonomous Know-Your-Customer (KYC) is required for hybrid banking. In this paper, an automated deep learning-based framework for interbank KYC in robot-based cyber-physical banking is proposed. A deep biometric architecture was used to model the customer's KYC and anonymise the collected visual data to ensure the customer's privacy. The symmetric-asymmetric encryption-decryption module in addition to the blockchain network was used for secure and decentralized transmission and validation of the biometric information. A high-capacity fragile watermarking algorithm based on the integer-to-integer discrete wavelet transform in combination with the Z6 and A6 lattice vector quantization for the secure transmission and storage of inperson banking documents is also proposed. The proposed framework was simulated and validated using a Pepper humanoid robot for the automated biometric-based collection of handwritten bank checks from customers adhering to COVID-19 pandemic safety guidelines. The biometric information of bank customers such as fingerprint and name is embedded as a watermark in the related bank documents using the proposed framework. The results show that the proposed security protection framework can embed more biometric data in bank documents in comparison with similar algorithms. Furthermore, the quality of the secured bank documents is 20% higher in comparison with other proposed algorithms. Also, the hierarchal visual information communication and storage module that anonymizes the identity of people in videos collected by robots can satisfy the privacy requirements of the banks. Overall, the proposed framework can provide a rapid, efficient, and cost-effective inter-bank solution for future in-person banking while adhering to the security requirements and banking regulations.

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