Restoration and Text Extraction for Enhanced Analysis of Vintage and Damaged Documents Using Deep Learning

Anand Magar, Rutuja Desai, Siddhi Deshmukh, Samarth Deshpande, Sakshi Dhamne

2025

Abstract

Restoration and analysis of vintage and damaged documents are crucial in preserving valuable historical and cultural records. In this paper, we present a novel approach that combines Document Enhancement Generative Adversarial Network (DE-GAN) for image restoration with Optical Character Recognition (OCR) for efficient text extraction. Our methodology focuses on restoring degraded documents by enhancing visual quality and legibility, allowing for more accurate text retrieval. By employing DE-GAN, we can mitigate various forms of document degradation, such as stains, faded ink and physical damage, while the OCR system effectively extracts the underlying text for further analysis. The proposed framework enhances the quality of historical document preservation and facilitates better data retrieval, ensuring that critical information is not lost due to document deterioration. Experimental results demonstrate significant improvements in both restoration quality and text extraction accuracy, making our method a robust solution for historical document analysis.

Paper Citation

in Harvard Style

Magar A., Desai R., Deshmukh S., Deshpande S. and Dhamne S. (2025). Restoration and Text Extraction for Enhanced Analysis of Vintage and Damaged Documents Using Deep Learning. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 708-714. DOI: 10.5220/0013600500004664

in Bibtex Style

@conference{incoft25,
author={Anand Magar and Rutuja Desai and Siddhi Deshmukh and Samarth Deshpande and Sakshi Dhamne},
title={Restoration and Text Extraction for Enhanced Analysis of Vintage and Damaged Documents Using Deep Learning},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={708-714},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013600500004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Restoration and Text Extraction for Enhanced Analysis of Vintage and Damaged Documents Using Deep Learning
SN - 978-989-758-763-4
AU - Magar A.
AU - Desai R.
AU - Deshmukh S.
AU - Deshpande S.
AU - Dhamne S.
PY - 2025
SP - 708
EP - 714
DO - 10.5220/0013600500004664
PB - SciTePress