AI-Driven Handwriting Identification and Verification Using Textural Features

Year : 2025 | Volume : 03 | Issue : 01 | Page : 35 44
    By

    Chandan Kumar Sonkar,

  • Vinod Kumar,

  1. Research Scholar, School of Computer Science and Engineering Galgotias University, Uttar Pradesh, india
  2. Associate Professor, School of Computer Science and Engineering Galgotias University, Uttar Pradesh, India

Abstract

The last few decades have seen handwriting recognition and verification earn their mark in areas like forensics, healthcare, education, and digital security. This study delves into the role of artificial intelligence (AI), machine learning (ML), and deep learning techniques in handwriting analysis. It highlights the extraction of textural features as a precursor to identifying narrows between original handwriting and its forgery, whereby a few distinctive patterns such as stroke width, curvature, and pressure variation are captured. Other methods, including CNNs and RNNs, enable appropriate feature extraction and classification providing solutions that can be automated, scalable, and quite accurate, while traditional machine learning techniques such as SVMs and KNNs complement deep learning through their provision of interpretable decision-making. The review underlines various ways by which the use of handwriting identification can find application in real-life scenarios such as forensic document examination, biometric authentication, healthcare diagnostics, and educational assessments, thus revealing its transformative potential across multiple parastatals.

Keywords: Biometric authentication, deep learning, feature extraction, handwriting verification, Handwriting identification, machine learning, textural

[This article belongs to International Journal of Electronics Automation ]

How to cite this article:
Chandan Kumar Sonkar, Vinod Kumar. AI-Driven Handwriting Identification and Verification Using Textural Features. International Journal of Electronics Automation. 2025; 03(01):35-44.
How to cite this URL:
Chandan Kumar Sonkar, Vinod Kumar. AI-Driven Handwriting Identification and Verification Using Textural Features. International Journal of Electronics Automation. 2025; 03(01):35-44. Available from: https://journals.stmjournals.com/ijea/article=2025/view=206661


References

  1. Zhai X, Chu X, Chai CS, Jong MS, Istenic A, Spector M, Liu JB, Yuan J, Li Y. A Review of Artificial Intelligence (AI) in Education from 2010 to 2020. Complexity. 2021;2021(1):8812542.
  2. Rehman A, Naz S, Razzak MI. Writer identification using machine learning approaches: a comprehensive review. Multimedia Tools and Applications. 2019 Apr;78:10889-931.
  3. Mukhamediev RI, Popova Y, Kuchin Y, Zaitseva E, Kalimoldayev A, Symagulov A, Levashenko V, Abdoldina F, Gopejenko V, Yakunin K, Muhamedijeva E. Review of artificial intelligence and machine learning technologies: Classification, restrictions, opportunities and challenges. Mathematics. 2022 Jul 22;10(15):2552.
  4. Dhali MA, Jansen CN, De Wit JW, Schomaker L. Feature-extraction methods for historical manuscript dating based on writing style development. Pattern Recognition Letters. 2020 Mar 1;131:413-20.
  5. Batool FE, Attique M, Sharif M, Javed K, Nazir M, Abbasi AA, Iqbal Z, Riaz N. Offline signature verification system: a novel technique of fusion of GLCM and geometric features using SVM. Multimedia Tools and Applications. 2024 Feb 1:1-20.
  6. Malakar S, Ghosh M, Bhowmik S, Sarkar R, Nasipuri M. A GA based hierarchical feature selection approach for handwritten word recognition. Neural Computing and Applications. 2020 Apr;32:2533-52.
  7. Akbal E. An automated environmental sound classification methods based on statistical and textural feature. Applied Acoustics. 2020 Oct 1;167:107413.
  8. Thangamariappan P, Pamila JC. Handwritten recognition by using machine learning approach. International Journal of Engineering Applied Sciences and Technology. 2020;4(11):2455-143.
  9. Pashine S, Dixit R, Kushwah R. Handwritten digit recognition using machine and deep learning algorithms. arXiv preprint arXiv:2106.12614. 2021 Jun 23.
  10. Porwal U, Fornes A, Shafait F. Advances in handwriting recognition. International Journal on Document Analysis and Recognition (IJDAR). 2022 Dec;25(4):241-3.
  11. Ott F, Wehbi M, Hamann T, Barth J, Eskofier B, Mutschler C. The onhw dataset: Online handwriting recognition from imu-enhanced ballpoint pens with machine learning. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2020 Sep 4;4(3):1-20.
  12. Alajrami E, Ashqar BA, Abu-Nasser BS, Khalil AJ, Musleh MM, Barhoom AM, Abu-Naser SS. Handwritten signature verification using deep learning.
  13. Yapıcı MM, Tekerek A, Topaloğlu N. Deep learning-based data augmentation method and signature verification system for offline handwritten signature. Pattern Analysis and Applications. 2021 Feb;24(1):165-79.
  14. Ghosh R. A Recurrent Neural Network based deep learning model for offline signature verification and recognition system. Expert Systems with Applications. 2021 Apr 15;168:114249.
  15. Ott F, Rügamer D, Heublein L, Hamann T, Barth J, Bischl B, Mutschler C. Benchmarking online sequence-to-sequence and character-based handwriting recognition from IMU-enhanced pens. International Journal on Document Analysis and Recognition (IJDAR). 2022 Dec;25(4):385-414.
  16. Sueiras J. Continuous offline handwriting recognition using deep learning models. arXiv preprint arXiv:2112.13328. 2021 Dec 26.
  17. Stuner B, Chatelain C, Paquet T. Handwriting recognition using cohort of LSTM and lexicon verification with extremely large lexicon. Multimedia Tools and Applications. 2020 Dec;79(45):34407-27.
  18. Al-Saffar A, Awang S, Al-Saiagh W, Al-Khaleefa AS, Abed SA. A Sequential Handwriting Recognition Model Based on a Dynamically Configurable CRNN. Sensors. 2021 Nov 2;21(21):7306.
Regular Issue Subscription Review Article
Volume 03
Issue 01
Received 21/03/2025
Accepted 29/03/2025
Published 08/04/2025
Publication Time 18 Days
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