Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)

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AI-Driven PDF Translation: Ensuring Accuracy, Efficiency, and Integrity

Authors
Sheela Chinchmalatpure1, *, Avishkar Ghodke1, Jineshwari Bagul1, Sakshi Dangade1, Devang Deshpande1
1Department of Artificial Intelligence and Data Science (AI&DS), Vishwakarma Institute of Technology, Pune, India
*Corresponding author. Email: sheela.chinchmalatpure@vit.edu
Corresponding Author
Sheela Chinchmalatpure
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_81How to use a DOI?
Keywords
PDF Translation; Layout Preservation; Multilingual Documentation; Machine Learning; Reinforcement Learning; Neural Machine Translation; Document Processing
Abstract

This paper presents a novel AI-driven framework for PDF translation that ensures accuracy, structural preservation, and security throughout the document processing pipeline. The system integrates advanced techniques such as deep learning-based watermark removal (achieving 92.5% detection accuracy), BERT-powered semantic analysis (94.2% contextual accuracy), and reinforcement learning-driven translation optimization (96.3% translation accuracy). Unlike traditional approaches that require format conversion, our method enables direct modifications within the original PDF, reducing processing time by 57% while preserving fonts, annotations, and layout integrity. A hybrid watermark removal system, combining OpenCV and GAN-based reconstruction, enhances text clarity by 87% while maintaining authenticity. Neutral Machine Translation (NMT) coupled with CRNN-based OCR module ensures 98% structural fidelity, even for image-embedded text. Post-processing features, including interactive user review (rated 4.8/5 for usability) and AI-driven layout restoration (97.6% accuracy), further refine output quality. Evaluation results demonstrate improved translation accuracy, faster processing times, and enhanced usability, positioning this approach as a significant advancement in automated PDF translation.

Copyright
© 2025 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.

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Volume Title
Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
Series
Advances in Computer Science Research
Publication Date
4 November 2025
ISBN
978-94-6463-858-5
ISSN
2352-538X
DOI
10.2991/978-94-6463-858-5_81How to use a DOI?
Copyright
© 2025 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  - Sheela Chinchmalatpure
AU  - Avishkar Ghodke
AU  - Jineshwari Bagul
AU  - Sakshi Dangade
AU  - Devang Deshpande
PY  - 2025
DA  - 2025/11/04
TI  - AI-Driven PDF Translation: Ensuring Accuracy, Efficiency, and Integrity
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 966
EP  - 977
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_81
DO  - 10.2991/978-94-6463-858-5_81
ID  - Chinchmalatpure2025
ER  -