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Paper ID:IJSREM62059

Document Image Restoration for OCR & Archives

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62059

Volume:

10 |

4 |

April 2026

Pages:

1-14

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM62059

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

KR

K. Rupali

Department of Computer Science and Engineering

RGUKT Basar, Hyderabad, India

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VB

Varshitha Bugga

Department of Computer Science and Engineering

RGUKT Basar, Hyderabad, India

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SA

Saisriya Alchetty

Department of Computer Science and Engineering

RGUKT Basar, Hyderabad, India

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Mentors

BL

Buddannagari Latha

Department of Computer Science and Engineering

RGUKT Basar, Hyderabad, India

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Abstract

Document digitization plays a critical role in preserving historical records, academic archives, and administrative documents. However, degraded document images often suffer from noise, faded ink, uneven illumination, and blur, which significantly reduce Optical Character Recog- nition (OCR) accuracy. This paper proposes an adaptive document restoration framework inte- grating quality-aware preprocessing, stroke-aware enhancement, and OCR-guided evaluation. Unlike traditional scanners that apply uniform enhancement, the proposed system analyzes document quality and dynamically routes images through specialized enhancement pipelines. Stroke-aware feature extraction preserves textual structures while suppressing background noise, improving readability and recognition performance. Experiments conducted on DIBCO datasets demonstrate improvements in structural clarity and text continuity compared to classical thresholding approaches. The proposed framework pro- vides a scalable and intelligent solution for document restoration and digital archiving applica- tions.

KEYWORDS

Keywords: Document Image Enhancement Stroke Awareness OCR Optimization Image Restoration Document Digitization

Cite This Article

K. Rupali, Varshitha Bugga, Saisriya Alchetty, Buddannagari Latha, "Document Image Restoration for OCR & Archives," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1-14, April 2026. https://doi.org/10.55041/IJSREM62059

Full Article/Conclusion

This work presents an adaptive document restoration framework integrating stroke-aware en- hancement and OCR-guided evaluation. The system dynamically adapts to document quality variations and preserves textual structures during enhancement. Experimental results demonstrate improved readability and recognition performance, making the approach suitable for archival digitization and intelligent document processing systems.

References

[1]     N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Transac- tions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979. [2]     J. Sauvola and M. Pietika¨inen, “Adaptive Document Image Binarization,” Pattern Recog- nition, vol. 33, no. 2, pp. 225–236, 2000. [3]     C. Wolf and J. Jolion, “Extraction and Recognition of Artificial Text in Multimedia Doc- uments,” Pattern Analysis and Applications, vol. 6, no. 4, pp. 309–326, 2004. [4]     I. Pratikakis, B. Gatos, and K. Ntirogiannis, “ICDAR Document Image Binarization Con- test (DIBCO),” International Conference on Document Analysis and Recognition (IC- DAR), pp. 1506–1510, 2009. [5]     N. R. Howe, “Document Binarization with Automatic Parameter Tuning,” International Journal on Document Analysis and Recognition, vol. 16, no. 3, pp. 247–258, 2013. [6]     O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomed- ical Image Segmentation,” International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241, 2015. [7]     R. Smith, “An Overview of the Tesseract OCR Engine,” International Conference on Document Analysis and Recognition (ICDAR), pp. 629–633, 2007. [8]     D. Kang, S. Kim, and H. Lee, “Deep Learning-Based Document Image Enhancement for OCR Accuracy Improvement,” IEEE Access, vol. 8, pp. 162593–162603, 2020. [9]     L. Xu, C. Lu, Y. Xu, and J. Jia, “Image Smoothing via L0 Gradient Minimization,” ACM Transactions on Graphics, vol. 30, no. 6, pp. 1–12, 2011. [10]     B. Su, S. Lu, and C. L. Tan, “Robust Document Image Binarization Technique for De- graded Document Images,” IEEE Transactions on Image Processing, vol. 22, no. 4, pp. 1408–1417, 2013. [11]     M. Li, T. Lv, J. Cui, et al., “TrOCR: Transformer-Based Optical Character Recogni- tion with Pretrained Models,” Proceedings of AAAI Conference on Artificial Intelligence, 2023. [12]     S. Kumar and M. Singh, “A Survey of Document Image Enhancement Techniques for OCR Systems,” Journal of Visual Communication and Image Representation, vol. 58, pp. 536–548, 2019.

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Publication Date

5/1/2026

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Review Rounds: 2

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