Evaluation of Generative AI Q&A Chatbot Chained to Optical Character Recognition Models for Financial Documents
Pages 101 - 110
Abstract
Financial statements are cornerstones of several analyses, such as loan applications, as well as for legal firms collecting evidence and analysis. They exert a significant influence on the decisions of these institutions. Streamlining the processing of these statements, regardless of their form—be it digital or hard copies—stands as a pivotal objective for banks and similar firms. This research explores the integration of Optical Character Recognition (OCR) and generative AI for automating the extraction of crucial financial data from bank statement images. Furthermore, we design an architecture to make a generic analysis possible on multiple types of financial documents by utilizing a classification model tailored to categorize bank statement documents. This facilitates seamless data preparation for subsequent analysis or model training. Emphasizing precision and efficiency, we investigate OCR model architectures designed specifically to enhance text extraction accuracy from low-resolution bank statement images. The study evaluates two different OCR model architectures—the accuracy of FSRCNN model being the best—achieving an accuracy above 93% in OCR. Additionally, we analyze a generative AI-based Q&A chatbot to simplify analysis for novice users.
References
[1]
Payne K. What is a bank statement? Forbes. 2023, https://www.forbes.com/advisor/banking/what-is-a-bank-statement/
[2]
R. Smith, ‘An overview of the Tesseract OCR engine’, in Ninth international conference on document analysis and recognition (ICDAR 2007), IEEE, 2007, pp. 629–633.
[3]
J. M. Jayoma, E. S. Moyon, and E. M. O. Morales, ‘OCR based document archiving and indexing using PyTesseract: A record management system for dswd caraga, Philippines’, in 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), IEEE, 2020, pp. 1–6.
[4]
I. Q. Habeeb, Z. Q. Al-Zaydi, and H. N. Abdulkhudhur, ‘Enhanced ensemble technique for optical character recognition’, in International Conference on New Trends in Information and Communications Technology Applications, Springer, 2018, pp. 213–225.
[5]
S. C. Park, M. K. Park, and M. G. Kang, ‘Super-resolution image reconstruction: a technical overview’, IEEE signal processing magazine, vol. 20, no. 3, pp. 21–36, 2003.
[6]
C. Dong, C. C. Loy, K. He, and X. Tang, ‘Image super-resolution using deep convolutional networks’, IEEE transactions on pattern analysis and machine intelligence, vol. 38, no. 2, pp. 295–307, 2015.
[7]
M. Chui, R. Roberts, and L. Yee, ‘Generative AI is here: How tools like ChatGPT could change your business’, Quantum Black AI by McKinsey, 2022.
[8]
J. Wang, ‘Git: A generative image-to-text transformer for vision and language’, arXiv preprint arXiv:2205.14100, 2022.
[9]
W. Yimyam, M. Ketcham, T. Jensuttiwetchakult, S. Hiranchan, P. Pramkeaw, and N. Chumuang, ‘Enhancing and Evaluating an Impact of OCR and Ontology on Financial Document Checking Process’, in 2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), IEEE, 2020, pp. 1–6.
[10]
J. Deng and Y. Lin, ‘The benefits and challenges of ChatGPT: An overview’, Frontiers in Computing and Intelligent Systems, vol. 2, no. 2, pp. 81–83, 2022.
[11]
J. Zhang, M. Raza, R. Khalid, R. Parveen, and E. H. Ramírez-Asís, ‘Impact of team knowledge management, problem solving competence, interpersonal conflicts, organizational trust on project performance, a mediating role of psychological capital.’, Annals of Operations Research, 2021.
[12]
Y. Du, Z. Hua, C. Liu, R. Lv, W. Jia, and M. Su, ‘ATR-FTIR combined with machine learning for the fast non-targeted screening of new psychoactive substances’, Forensic Science International, p. 111761, 2023.
[13]
J. Yousefi, ‘Image binarization using Otsu thresholding algorithm’, Ontario, Canada: University of Guelph, vol. 10, 2011.
Index Terms
- Evaluation of Generative AI Q&A Chatbot Chained to Optical Character Recognition Models for Financial Documents
Recommendations
Off-line cursive handwritten Tamil character recognition
In spite of several advancements in technologies pertaining to Optical character recognition, handwriting continues to persist as means of documenting information for day-to-day life. The process of segmentation and recognition pose quiets a lot of ...
Gujarati handwritten numeral optical character reorganization through neural network
This paper deals with an optical character recognition (OCR) system for handwritten Gujarati numbers. One may find so much of work for Indian languages like Hindi, Kannada, Tamil, Bangala, Malayalam, Gurumukhi etc, but Gujarati is a language for which ...
A New System for Recognition of Handwritten Persian Bank Checks
ICDAR '11: Proceedings of the 2011 International Conference on Document Analysis and RecognitionIn this paper, a novel system for segmentation and recognition of handwritten Persian bank checks is presented. Our focus in this paper is on segmentation and recognition of handwritten courtesy amounts and dates of Persian checks. We present the ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
January 2024
210 pages
ISBN:9798400716546
DOI:10.1145/3647750
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 12 April 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICMLSC 2024
ICMLSC 2024: 2024 The 8th International Conference on Machine Learning and Soft Computing
January 26 - 28, 2024
Singapore, Singapore
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 97Total Downloads
- Downloads (Last 12 months)97
- Downloads (Last 6 weeks)24
Reflects downloads up to 11 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format