Developing a Third-Party API to Enhance Image Documents at the University of Mosul Data Center
DOI:
https://doi.org/10.47839/ijc.23.4.3771Keywords:
Cloud Data Integration, Document Enhancement, Software Engineering IntegrationAbstract
Data integration plays a crucial role in SWE as it guarantees the proper functioning of multiple system, applications, and datasets. This integration is merit since it helps in developing a single reliable source of information from many, and the lack of data integration results in inefficient and time-consuming manual operations such as redundant data input, document retrieval issues, and data handling mistakes. This paper presents a comprehensive methodology for enhancing document images of Mosul University data centers and their quality assessment through integration with a cloud platform. The proposed approach consists of two main stages: the integration stage and the cloud platform stage. During the integration stage, the university's data center is seamlessly connected to a cloud platform via a RESTful API, allowing for efficient data interchange while maintaining data integrity and security. Next, image document representations are received in the cloud platform stage and undergo enhancement processes with specific tools and algorithms designed for image enhancement. The approaches employed in this study encompass image scaling, similarity evaluation utilizing the Histogram of Oriented Gradients (HOG) algorithm, image warping employing the FLANN (Fast Library for Approximate Nearest Neighbors) algorithm, and image quality enhancement by the application of a Laplacian sharpening filter. The proposed integrated cloud-based document enhancement has shown exceptional efficiency in data sharing and precise analysis and enhancement of picture documents within the university's data center.
References
C. Fischer et al., “Mining big data in education: Affordances and challenges,” Review of Research in Education, vol. 44, no. 1, pp. 130–160, 2020. https://doi.org/10.3102/0091732X20903304.
P. Kaushal, “Integrated information systems in higher education: Systematic review and research opportunities,” Anwesh, vol. 4, no. 2, p. 28, 2019.
N. A. H. M. Rodzi, M. S. Othman, and L. M. Yusuf, “Significance of data integration and ETL in business intelligence framework for higher education,” Proceedings of the 2015 IEEE International Conference on Science in Information Technology (ICSITech), 2015, pp. 181–186. https://doi.org/10.1109/ICSITech.2015.7407800.
P. Groth, A. Loizou, A. J. G. Gray, C. Goble, L. Harland, and S. Pettifer, “API-centric linked data integration: The open PHACTS discovery platform case study,” Journal of Web Semantics, vol. 29, pp. 12–18, 2014. https://doi.org/10.1016/j.websem.2014.03.003.
B. Kwofie, Cloud computing opportunities, risks and challenges with regard to Information Security in the context of developing countries: A case study of Ghana, Master Thesis, Luleå University of Technology, Sweden, 2013. [Online]. Available at: https://www.coursehero.com/file/53110918/FULLTEXT02pdf.
D. L. Goodhue, M. D. Wybo, and L. J. Kirsch, “The impact of data integration on the costs and benefits of information systems,” MIS quarterly, pp. 293–311, 1992. https://doi.org/10.2307/249530.
A. Bhawiyuga, D. P. Kartikasari, K. Amron, O. B. Pratama, and M. W. Habibi, “Architectural design of IoT-cloud computing integration platform,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 17, no. 3, pp. 1399–1408, 2019. https://doi.org/10.12928/telkomnika.v17i3.11786.
A. Bhawiyuga, S. A. Kharisma, B. J. Santoso, D. P. Kartikasari, and A. P. Kirana, “Cloud-based middleware for supporting batch and stream access over smart healthcare wearable device,” Bulletin of Electrical Engineering and Informatics, vol. 9, no. 5, pp. 1990–1997, 2020. https://doi.org/10.11591/eei.v9i5.1978.
M. M. Othman and A. El-Mousa, “Internet of things & cloud computing internet of things as a service approach,” Proceedings of the in 2020 11th IEEE International Conference on Information and Communication Systems (ICICS), 2020, pp. 318–323. https://doi.org/10.1109/ICICS49469.2020.239503.
M. Awadalla, F. Kausar, and R. Ahshan, “Developing an IoT platform for the elderly health care,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 4, pp. 410-417, 2021. https://doi.org/10.14569/IJACSA.2021.0120453.
R. Z. Frantz, R. Corchuelo, V. Basto‐Fernandes, F. Rosa‐Sequeira, F. Roos‐Frantz, and J. L. Arjona, “A cloud‐based integration platform for enterprise application integration: A model‐driven engineering approach,” Softw Pract Exp, vol. 51, no. 4, pp. 824–847, 2021. https://doi.org/10.1002/spe.2916.
B. Maharathi, F. Mir, K. Hosur, and J. A. Loeb, “INTUITION: A data platform to integrate human epilepsy clinical care and support for discovery,” Front Digit Health, vol. 5, p. 1091508, 2023. https://doi.org/10.3389/fdgth.2023.1091508.
X. Liu, G. Meng, B. Fan, S. Xiang, and C. Pan, “Geometric rectification of document images using adversarial gated unwarping network,” Pattern Recognition, vol. 108, p. 107576, 2020. https://doi.org/10.1016/j.patcog.2020.107576.
G.-W. Xie, F. Yin, X.-Y. Zhang, and C.-L. Liu, “Dewarping document image by displacement flow estimation with fully convolutional network,” Proceedings of the 14th IAPR International Workshop on Document Analysis Systems, DAS’2020, Wuhan, China, July 26–29, 2020, Proceedings 14, Springer, 2020, pp. 131–144. https://doi.org/10.1007/978-3-030-57058-3_10.
S. Das et al., “End-to-end piece-wise unwarping of document images,” Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 4268–4277. https://doi.org/10.1109/ICCV48922.2021.00423.
W. Xiong, X. Jia, D. Yang, M. Ai, L. Li, and S. Wang, “DP-LinkNet: A convolutional network for historical document image binarization,” KSII Transactions on Internet and Information Systems (TIIS), vol. 15, no. 5, pp. 1778–1797, 2021. https://doi.org/10.3837/tiis.2021.05.011.
R. Elanwar, W. Qin, M. Betke, and D. Wijaya, “Extracting text from scanned Arabic books: a large-scale benchmark dataset and a fine-tuned Faster-R-CNN model,” International Journal on Document Analysis and Recognition (IJDAR), vol. 24, no. 4, pp. 349–362, 2021. https://doi.org/10.1007/s10032-021-00382-4.
M. W. Labib and K. M. Amin, “Warped document image correction based on checkboard pattern and geometric transformation,” IJCI International Journal of Computers and Information, vol. 8, no. 1, pp. 30–54, 2021.
F. Verhoeven, T. Magne, and O. Sorkine-Hornung, “Neural Document Unwarping using Coupled Grids,” arXiv preprint arXiv:2302.02887, 2023.
C. H. Nachappa, N. S. Rani, P. B. Pati, and M. Gokulnath, “Adaptive dewarping of severely warped camera-captured document images based on document map generation,” International Journal on Document Analysis and Recognition (IJDAR), vol. 26, no. 2, pp. 149–169, 2023. https://doi.org/10.1007/s10032-022-00425-4.
S. Ghaffari, P. Soleimani, K. F. Li, and D. W. Capson, “Analysis and comparison of FPGA-based histogram of oriented gradients implementations,” IEEE Access, vol. 8, pp. 79920–79934, 2020. https://doi.org/10.1109/ACCESS.2020.2989267.
S. Kaneko, Y. Satoh, and S. Igarashi, “Using selective correlation coefficient for robust image registration,” Pattern Recognition, vol. 36, no. 5, pp. 1165–1173, 2003. https://doi.org/10.1016/S0031-3203(02)00081-X.
S.-Y. Hwang, J. H. Lee, C. S. Ha, M. Yang, and J. H. Choi, “Real-Time 2D Orthomosaic Mapping from Drone-Captured Images Using Feature-Based Sequential Image Registration,” 2023, https://doi.org/10.20944/preprints202312.0190.v1.
S. Wen, X. Wang, W. Zhang, G. Wang, M. Huang, and B. Yu, “Structure preservation and seam optimization for parallax-tolerant image stitching,” IEEE Access, vol. 10, pp. 78713–78725, 2022. https://doi.org/10.1109/ACCESS.2022.3194245.
Y. Gao, “Article identification for inventory list in a warehouse environment,” Master Thesis, Halmstad University, Sweden, 2014. https://www.diva-portal.org/smash/get/diva2:766369/FULLTEXT01.pdf.
K. Belattar and S. Mostefai, “Similarity measures for content-based dermoscopic image retrieval: A comparative study,” Proceedings of the 2015 First IEEE International Conference on New Technologies of Information and Communication (NTIC), 2015, pp. 1–6. https://doi.org/10.1109/NTIC.2015.7368761.
N. Patel, A. Shah, M. Mistry, and K. Dangarwala, “A study of digital image filtering techniques in spatial image processing,” Proceedings of the 2014 International Conference on Convergence of Technology (I2CT), 2014, pp. 1–6.
Y. Li, W. Zheng, X. Liu, Y. Mou, L. Yin, and B. Yang, “Research and improvement of feature detection algorithm based on FAST,” Rend Lincei Sci Fis Nat, vol. 32, no. 4, pp. 775–789, 2021. https://doi.org/10.1007/s12210-021-01020-1.
Z. N. Sultani and B. N. Dhannoon, “Modified bag of visual words model for image classification,” Al-Nahrain Journal of Science, vol. 24, no. 2, pp. 78–86, 2021. https://doi.org/10.22401/ANJS.24.2.11.
A. Jakubović and J. Velagić, “Image feature matching and object detection using brute-force matchers,” Proceedings of the 2018 IEEE International Symposium ELMAR, 2018, pp. 83–86. https://doi.org/10.23919/ELMAR.2018.8534641.
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