Vol. 5 No. 1 (2025): Issue 5
Articles

Efficient Optical Character Recognition through Radial Basis Function

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  • Anouk Jansen,
  • Emily Carter,
  • Liam Wilson,
  • Sophie Thompson
Emily Carter
School of Information Science, Charles Sturt University, Bathurst, NSW 2795, Australia
Liam Wilson
Institute of Advanced Computing, University of New England, Armidale, NSW 2351, Australia
Sophie Thompson
Centre for Photonic Research, Southern Cross University, Lismore, NSW 2480, Australia
DOI: https://doi.org/10.71070/oaml.v5i1.140

Published 2025-04-17

Keywords

  • Optical Character Recognition,
  • Document Digitization,
  • Text Mining,
  • Radial Basis Function Networks,
  • Image Processing Techniques

How to Cite

Jansen, A., Carter, E., Wilson, L., & Thompson, S. (2025). Efficient Optical Character Recognition through Radial Basis Function. Optimizations in Applied Machine Learning, 5(1). https://doi.org/10.71070/oaml.v5i1.140
Creative Commons License

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

Abstract

Optical Character Recognition (OCR) is a crucial technology for converting images of text into editable and searchable data. The increasing demand for efficient OCR systems in various fields, such as document digitization and text mining, highlights the significance of optimizing OCR processes. However, existing OCR methods often face challenges in accurately recognizing characters from distorted or low-quality images, limiting their practical applicability. In this context, this paper proposes a novel approach for efficient OCR based on Radial Basis Function (RBF) networks. By leveraging the capabilities of RBF networks in nonlinear mapping and pattern recognition, our method aims to enhance the accuracy and efficiency of character recognition tasks. The innovative framework introduced in this study combines the robustness of RBF networks with advanced image processing techniques to improve OCR performance, particularly in challenging image conditions. This research contributes to the optimization of OCR systems, offering a promising solution for enhancing the effectiveness of character recognition processes in real-world applications.

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