CNN-BILSTM Architectures for Handwritten Signature Verification: Insights and Innovations

Year : 2025 | Volume : 12 | Issue : 02 | Page : 43 50
    By

    Manmohan Jatav,

  • Shivank Kumar Soni,

  1. Research Scholar, Department of Computer Science and Engineering, Oriental Institute of Science and Technology, Bhopal, Madhya Pradesh, India
  2. Assistant Professor, Department of Computer Science and Engineering, Oriental Institute of Science and Technology, Bhopal, Madhya Pradesh, India

Abstract

Verifying handwritten signatures is essential for identity authentication to guard against fraud and guarantee security across a range of platforms. The approaches and developments in handwritten signature verification are examined in this review, with an emphasis on both offline and online techniques. While online methods use dynamic information like stroke order and speed, collected by specialized devices, offline verification uses scanned photographs of signatures. Even if technology is moving toward online approaches more and more, offline systems are still needed, especially in settings where paper is used extensively. The difficulties of offline signature verification are discussed in the study, with a focus on the difficulties of feature extraction in the absence of dynamic data inputs. It also examines the development of handwritten text recognition, emphasizing the function of recurrent and convolutional neural networks, as well as its uses in optical character recognition (OCR). We evaluate and analyze various deep learning methods for handwritten document recognition, including advantages and disadvantages. Offline Handwritten Text Recognition (OHTR) has a comprehensive framework that includes phases for feature extraction, classification, and preprocessing. Lastly, a review of current developments and their consequences is given regarding the integration of CNNs and LSTMs in handwriting recognition.

Keywords: Handwritten signature verification, offline signature verification, online signature verification, convolutional neural networks (CNNs), recurrent neural networks (RNNs), optical character recognition (OCR)

[This article belongs to Journal of Image Processing & Pattern Recognition Progress ]

How to cite this article:
Manmohan Jatav, Shivank Kumar Soni. CNN-BILSTM Architectures for Handwritten Signature Verification: Insights and Innovations. Journal of Image Processing & Pattern Recognition Progress. 2025; 12(02):43-50.
How to cite this URL:
Manmohan Jatav, Shivank Kumar Soni. CNN-BILSTM Architectures for Handwritten Signature Verification: Insights and Innovations. Journal of Image Processing & Pattern Recognition Progress. 2025; 12(02):43-50. Available from: https://journals.stmjournals.com/joipprp/article=2025/view=208920


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Regular Issue Subscription Original Research
Volume 12
Issue 02
Received 04/02/2025
Accepted 07/03/2025
Published 28/04/2025
Publication Time 83 Days
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