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- Vol. 5 Issue 3
- Article
A Hybrid Text-Extraction and Transformer-Based Classification Pipeline with Asynchronous Fast API Serving for Academic Document Management
Authors
1 2 Department of Computer Science and Engineering, CSMSS Chh. Shahu College of Engineering, Maharashtra, India.
Article Information
- Published In
- Volume 5, Issue 3 (2026)
- Published Online
- September-December 2026
- Pages
- 835-840
- Article Type
- Original Article
References
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3. Y. Liu, M. Ott, N. Goyal, et al., “RoBERTa: a robustly optimized BERT pretraining approach,” arXiv:1907.11692, 2019.4. R. Smith, “An overview of the Tesseract OCR engine,” in Proc. 9th Int. Conf. Document Analysis and Recognition (ICDAR), vol. 2, 2007, pp. 629–633.
5. R. Kasturi, V. Govindaraju, J. Hu, M. Cheriet, et al., “Document image analysis: a primer,” Sādhanā, vol. 27, no. 1, pp. 3–22, 2002.
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8. K. Thirunavukkarasu and H. Shankar, “A computer vision powered OCR framework for extracting tabular data from scanned PDFs,” Int. J. Research Trends and Innovation, vol. 10, no. 4, pp. d126–d130, 2025.
9. T. Wolf et al., “Transformers: state-of-the-art natural language processing,” in Proc. EMNLP: System Demonstrations, 2020, pp. 38–45.
10. S. Ramírez, “FastAPI framework documentation,” 2023. [Online]. Available: https://fastapi.tiangolo.com
11. “PyMuPDF documentation,” Artifex Software. [Online]. Available: https://pymupdf.readthedocs.io
12. “Apache Tika documentation,” The Apache Software Foundation. [Online]. Available: https://tika.apache.org
13. G. Bradski, “The OpenCV library,” Dr. Dobb’s Journal of Software Tools, 2000.
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2. V. Sanh, L. Debut, J. Chaumond, and T. Wolf, “DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter,” arXiv:1910.01108, 2019.
3. Y. Liu, M. Ott, N. Goyal, et al., “RoBERTa: a robustly optimized BERT pretraining approach,” arXiv:1907.11692, 2019.4. R. Smith, “An overview of the Tesseract OCR engine,” in Proc. 9th Int. Conf. Document Analysis and Recognition (ICDAR), vol. 2, 2007, pp. 629–633.
5. R. Kasturi, V. Govindaraju, J. Hu, M. Cheriet, et al., “Document image analysis: a primer,” Sādhanā, vol. 27, no. 1, pp. 3–22, 2002.
6. T. Hegghammer, “OCR with Tesseract, Amazon Textract, and Google Document AI: a benchmarking experiment,” J. Computational Social Science, vol. 5, pp. 861–882, 2022.
7. C. Irimia, F. Harbuzariu, I. Hazi, and A. Iftene, “Official document identification and data extraction using templates and OCR,” Procedia Computer Science, vol. 207, pp. 1571–1580, 2022.
8. K. Thirunavukkarasu and H. Shankar, “A computer vision powered OCR framework for extracting tabular data from scanned PDFs,” Int. J. Research Trends and Innovation, vol. 10, no. 4, pp. d126–d130, 2025.
9. T. Wolf et al., “Transformers: state-of-the-art natural language processing,” in Proc. EMNLP: System Demonstrations, 2020, pp. 38–45.
10. S. Ramírez, “FastAPI framework documentation,” 2023. [Online]. Available: https://fastapi.tiangolo.com
11. “PyMuPDF documentation,” Artifex Software. [Online]. Available: https://pymupdf.readthedocs.io
12. “Apache Tika documentation,” The Apache Software Foundation. [Online]. Available: https://tika.apache.org
13. G. Bradski, “The OpenCV library,” Dr. Dobb’s Journal of Software Tools, 2000.
14. M. Jones, J. Bradley, and N. Sakimura, “JSON Web Token (JWT),” RFC 7519, IETF, 2015.
15. N. Provos and D. Mazières, “A future-adaptable password scheme,” in Proc. USENIX Annual Technical Conf., 1999, pp. 81–91.
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