Handwritten Signature Verification System using machine Learning Approach- A Review of Literature

Pooja Gaikwad; Kashaf Pathan; Purva Patil; Laxmi Pagare; Prof. R.P. Dahake

Handwritten Signature Verification System using machine Learning Approach- A Review of Literature

April 2021

Vol-7, Issue-2

Paper ID: 14036

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Computer Engineering

Keywords

Offline handwritten signature classification algorithms artificial intelligence Random forest classifier.

Abstract

ABSTRACT Abstract- This system proposes a feasible solution to verify handwritten signatures using various machine learning approaches. The scope has been scaling down to offline signatures which contains static inputs and outputs. Several classification methods such as Multinomial Naive Bayes Classifier (MNBC), Bernoulli Naive Bayes Classifier (BNBC), Logistic Regression Classifier (LRC), Stochastic Gradient Descent Classifier (SGDC), and Random Forest Classifier (RFC) were implemented to identify the most suitable classifier to verify handwritten signatures. The classifiers were pre-trained and tested using a handwritten signature database available for public use available on the Kaggle website. The best performance was obtained from RFC with and accuracy score of more than 0.6. For average, the framework made has been successful in verifying handwritten signature images provided with an extensive precision level.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

#	Name	Institute / Affiliation
1	Pooja Gaikwad	MET’s Institute of Engineering, Nashik, INDIA
2	Kashaf Pathan	MET’s Institute of Engineering, Nashik, INDIA
3	Purva Patil	MET’s Institute of Engineering, Nashik, INDIA
4	Laxmi Pagare	MET’s Institute of Engineering, Nashik, INDIA
5	Prof. R.P. Dahake	MET’s Institute of Engineering, Nashik, INDIA

How to Cite

Use the following formats to cite this article in your research.

APA Style

Gaikwad, Pooja, Pathan, Kashaf, Patil, Purva, Pagare, Laxmi, & Dahake, Prof. R.P. (2021). Handwritten Signature Verification System using machine Learning Approach- A Review of Literature. International Journal of Advance Research and Innovative Ideas In Education, 7(2), 1328-1332.

MLA Style

Gaikwad, Pooja, et al. "Handwritten Signature Verification System using machine Learning Approach- A Review of Literature." International Journal of Advance Research and Innovative Ideas In Education, vol. 7, no. 2, 2021, pp. 1328-1332.

IEEE Style

Pooja Gaikwad, Kashaf Pathan, Purva Patil, Laxmi Pagare, and Prof. R.P. Dahake, "Handwritten Signature Verification System using machine Learning Approach- A Review of Literature," International Journal of Advance Research and Innovative Ideas In Education, vol. 7, no. 2, pp. 1328-1332, 2021.

Vancouver Style

Gaikwad Pooja, Pathan Kashaf, Patil Purva, Pagare Laxmi, Dahake Prof. R.P.. Handwritten Signature Verification System using machine Learning Approach- A Review of Literature. International Journal of Advance Research and Innovative Ideas In Education. 2021;7(2):1328-1332.

Harvard Style

Gaikwad, Pooja, Pathan, Kashaf, Patil, Purva, Pagare, Laxmi, & Dahake, Prof. R.P. (2021) 'Handwritten Signature Verification System using machine Learning Approach- A Review of Literature', International Journal of Advance Research and Innovative Ideas In Education, 7(2), pp. 1328-1332.

Chicago Style

Gaikwad, Pooja, et al. "Handwritten Signature Verification System using machine Learning Approach- A Review of Literature." International Journal of Advance Research and Innovative Ideas In Education 7, no. 2 (2021): 1328-1332.

Turabian Style

Gaikwad, Pooja, et al. "Handwritten Signature Verification System using machine Learning Approach- A Review of Literature." International Journal of Advance Research and Innovative Ideas In Education 7, no. 2 (2021): 1328-1332.