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- Vol. 5 Issue 3
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A Hybrid Pytesseract–Easy OCR Pipeline with Semaphore-Controlled Asynchronous Serving for on-Premise Document Data Extraction
Authors
1 2 Department of Computer Science and Engineering, CSMSS Chh. Shahu College of Engineering, Sambhajinagar, Maharashtra, India.
Article Information
- Published In
- Volume 5, Issue 3 (2026)
- Published Online
- September-December 2026
- Pages
- 841-845
- Article Type
- Original Article
References
1. 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.
2. Jaided AI, “EasyOCR: ready-to-use OCR,” GitHub repository, 2023. [Online]. Available: https://github.com/JaidedAI/EasyOCR
3. Y. Baek, B. Lee, D. Han, S. Yun, and H. Lee, “Character region awareness for text detection,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2019, pp. 9365–9374.
4. B. Shi, X. Bai, and C. Yao, “An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 11, pp. 2298–2304, 2017.
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. G. Bradski, “The OpenCV library,” Dr. Dobb's Journal of Software Tools, 2000.
7. T. Hegghammer, “OCR with Tesseract, Amazon Textract, and Google Document AI: a benchmarking experiment,” J. Computational Social Science, vol. 5, pp. 861–882, 2022.
8. 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.
9. 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.
10. S. Ramírez, “FastAPI framework documentation,” 2023. [Online]. Available: https://fastapi.tiangolo.com
11. Python Software Foundation, “asyncio — asynchronous I/O,” Python 3 Documentation, 2024.
12. M. Li, T. Lv, J. Chen, L. Cui, Y. Lu, D. Florencio, C. Zhang, Z. Li, and F. Wei, “TrOCR: transformer-based optical character recognition with pre-trained models,” in Proc. AAAI Conf. Artificial Intelligence, 2023.
13. Paszke et al., “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (NeurIPS), vol. 32, 2019.
2. Jaided AI, “EasyOCR: ready-to-use OCR,” GitHub repository, 2023. [Online]. Available: https://github.com/JaidedAI/EasyOCR
3. Y. Baek, B. Lee, D. Han, S. Yun, and H. Lee, “Character region awareness for text detection,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2019, pp. 9365–9374.
4. B. Shi, X. Bai, and C. Yao, “An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 11, pp. 2298–2304, 2017.
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. G. Bradski, “The OpenCV library,” Dr. Dobb's Journal of Software Tools, 2000.
7. T. Hegghammer, “OCR with Tesseract, Amazon Textract, and Google Document AI: a benchmarking experiment,” J. Computational Social Science, vol. 5, pp. 861–882, 2022.
8. 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.
9. 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.
10. S. Ramírez, “FastAPI framework documentation,” 2023. [Online]. Available: https://fastapi.tiangolo.com
11. Python Software Foundation, “asyncio — asynchronous I/O,” Python 3 Documentation, 2024.
12. M. Li, T. Lv, J. Chen, L. Cui, Y. Lu, D. Florencio, C. Zhang, Z. Li, and F. Wei, “TrOCR: transformer-based optical character recognition with pre-trained models,” in Proc. AAAI Conf. Artificial Intelligence, 2023.
13. Paszke et al., “PyTorch: an imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems (NeurIPS), vol. 32, 2019.
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