Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)

<Previous Article In Volume
Next Article In Volume>

Extraction of Bank Cheque Fields Based on Faster R-CNN

Authors
Hakim A. Abdo1, 2, Ahmed Abdu3, Ramesh Manza1, Shobha Bawiskar4, *
1Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India
2Hodeidah University, Al-Hudaydah, Yemen
3Northwestern Polytechnical University, Xi’an, China
4Government Institute of Forensic Science, Aurangabad, India
*Corresponding author. Email: shobha_bawiskar@yahoo.co.in
Corresponding Author
Shobha Bawiskar
Available Online 10 August 2023.
DOI
10.2991/978-94-6463-196-8_12How to use a DOI?
Keywords
cheque fields; Object detection; Faster R-CNN
Abstract

The cheque field extraction is a critical step in automating bank cheque processing and is the first step in implementing a cheque recognition system. Many approaches for extracting the bank cheques components have been suggested. However, the complexity of the backdrop, the design variety of bank cheques, the variety of font sizes, and different patterns of writing remain a difficulty that necessitates the employment of precise algorithms. In this paper, we present a novel approach to extract the bank cheque components, in presented approach we used an innovative model called Faster R-CNN. This model represents the pinnacle of object recognition since it eliminates the need to manually extract image features and instead segments images to provide candidate region suggestions automatically. The IDRBT Cheque Image Dataset is used to train and test the Faster R-CNN model. The findings demonstrate that the model is capable of properly detecting the bank cheque fields. The extraction of bank cheque fields using Faster R-CNN achieves an accuracy of 97.4%, which outperforms other techniques.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
Series
Advances in Intelligent Systems Research
Publication Date
10 August 2023
ISBN
978-94-6463-196-8
ISSN
1951-6851
DOI
10.2991/978-94-6463-196-8_12How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Hakim A. Abdo
AU  - Ahmed Abdu
AU  - Ramesh Manza
AU  - Shobha Bawiskar
PY  - 2023
DA  - 2023/08/10
TI  - Extraction of Bank Cheque Fields Based on Faster R-CNN
BT  - Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
PB  - Atlantis Press
SP  - 130
EP  - 139
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-196-8_12
DO  - 10.2991/978-94-6463-196-8_12
ID  - Abdo2023
ER  -