References
- 김동규, 이동욱, 박장원, 오성우, 권성준, 이인용, 최동원. (2022), KB-BERT: 금융 특화 한국어 사전학습 언어모델과 그 응용, 지능정보연구 28(2), 191-206. https://doi.org/10.13088/JIIS.2022.28.2.191
- 이정선, 서보일, 권영욱. (2021), 인공지능이 의사결정에 미치는 영향에 관한 연구: 인간과 인공지능의 협업 및 의사결정자의 성격 특성을 중심으로, 지능정보연구 27(3), 231-252. https://doi.org/10.13088/JIIS.2021.27.3.231
- 최은주, 이준영, 한인구. (2020), 딥러닝 오픈소스 프레임워크의 사례연구를 통한 도입 전략도출, 지능정보연구 26(4), 27-65. https://doi.org/10.13088/JIIS.2020.26.4.027
- Abdollahi Vayghan, L., Saied, M. A., Toeroe, M., & Khendek, F. (2019). Microservice Based Architecture: Towards High-Availability for Stateful Applications with Kubernetes. 2019 IEEE 19th International Conference on Software Quality, Reliability and Security (QRS), 176-185. https://doi.org/10.1109/QRS.2019.00034
- Akyurek, E., Dayanik, E., & Yuret, D. (2019). Morphological Analysis Using a Sequence Decoder. Transactions of the Association for Computational Linguistics, 7, 567-579. https://doi.org/10.1162/tacl_a_00286
- Al-Badr, B., & Mahmoud, S. A. (1995). Survey and bibliography of Arabic optical text recognition. Signal Processing, 41(1), 49-77. https://doi.org/10.1016/0165-1684(94)00090-M
- Appalaraju, S., Jasani, B., Kota, B. U., Xie, Y., & Manmatha, R. (2021). DocFormer: End-to-End Transformer for Document Understanding. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 973-983. https://doi.org/10.1109/ICCV48922.2021.00103
- Armenise, V. (2015). Continuous Delivery with Jenkins: Jenkins Solutions to Implement Continuous Delivery. 2015 IEEE/ACM 3rd International Workshop on Release Engineering, 24-27. https://doi.org/10.1109/RELENG.2015.19
- Atienza, R. (2021). Vision Transformer for Fast and Efficient Scene Text Recognition (pp. 319-334). https://doi.org/10.1007/978-3-030-86549-8_21
- Baek, J., Kim, G., Lee, J., Park, S., Han, D., Yun, S., Oh, S. J., & Lee, H. (2019). What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis.
- Baek, Y., Lee, B., Han, D., Yun, S., & Lee, H. (2019). Character Region Awareness for Text Detection. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9357-9366. https://doi.org/10.1109/CVPR.2019.00959
- Bartz, C., Yang, H., & Meinel, C. (2017). STN-OCR: A single Neural Network for Text Detection and Text Recognition.
- Bisong, E. (2019). Kubeflow and Kubeflow Pipelines. In Building Machine Learning and Deep Learning Models on Google Cloud Platform (pp. 671-685). Apress. https://doi.org/10.1007/978-1-4842-4470-8_46
- Bookstein, F. L. (1989). Principal warps: thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(6), 567-585. https://doi.org/10.1109/34.24792
- Chauhan, R., Ghanshala, K. K., & Joshi, R. C. (2018). Convolutional Neural Network (CNN) for Image Detection and Recognition. 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), 278-282. https://doi.org/10.1109/ICSCCC.2018.8703316
- Chen, A., Chow, A., Davidson, A., DCunha, A., Ghodsi, A., Hong, S. A., Konwinski, A., Mewald, C., Murching, S., Nykodym, T., Ogilvie, P., Parkhe, M., Singh, A., Xie, F., Zaharia, M., Zang, R., Zheng, J., & Zumar, C. (2020). Developments in MLflow. Proceedings of the Fourth International Workshop on Data Management for End-to-End Machine Learning, 1-4. https://doi.org/10.1145/3399579.3399867
- Cortes Rios, J. C., Kopec-Harding, K., Eraslan, S., Page, C., Haines, R., Jay, C., & Embury, S. M. (2019). A Methodology for Using GitLab for Software Engineering Learning Analytics. 2019 IEEE/ACM 12th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE), 3-6. https://doi.org/10.1109/CHASE.2019.00009
- Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018). Generative Adversarial Networks: An Overview. IEEE Signal Processing Magazine, 35(1), 53-65. https://doi.org/10.1109/MSP.2017.2765202
- Dieleman, S., Willett, K. W., & Dambre, J. (2015). Rotation-invariant convolutional neural networks for galaxy morphology prediction. Monthly Notices of the Royal Astronomical Society, 450(2), 1441-1459. https://doi.org/10.1093/mnras/stv632
- Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2020). An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale.
- Ganis, M. D., Wilson, C. L., & Blue, J. L. (1998). Neural network-based systems for handprint OCR applications. IEEE Transactions on Image Processing, 7(8), 1097-1112. https://doi.org/10.1109/83.704304
- Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image Style Transfer Using Convolutional Neural Networks. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2414-2423. https://doi.org/10.1109/CVPR.2016.265
- Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. 2014 IEEE Conference on Computer Vision and Pattern Recognition, 580-587. https://doi.org/10.1109/CVPR.2014.81
- Gos, K., & Zabierowski, W. (2020). The Comparison of Microservice and Monolithic Architecture. 2020 IEEE XVIth International Conference on the Perspective Technologies and Methods in MEMS Design (MEMSTECH), 150-153. https://doi.org/10.1109/MEMSTECH49584.2020.9109514
- Graves, A., Fernandez, S., Gomez, F., & Schmidhuber, J. (2006). Connectionist temporal classification. Proceedings of the 23rd International Conference on Machine Learning - ICML '06, 369-376. https://doi.org/10.1145/1143844.1143891
- Hewage, N., & Meedeniya, D. (2022). Machine Learning Operations: A Survey on MLOps Tool Support. https://doi.org/10.48550/arXiv.2202.10169
- Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
- Howard, J., & Ruder, S. (2018). Universal Language Model Fine-tuning for Text Classification. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 328-339. https://doi.org/10.18653/v1/P18-1031
- Huang, Z., Chen, K., He, J., Bai, X., Karatzas, D., Lu, S., & Jawahar, C. v. (2019). ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction. 2019 International Conference on Document Analysis and Recognition (ICDAR), 1516-1520. https://doi.org/10.1109/ICDAR.2019.00244
- Kim, D., Kwak, M., Won, E., Shin, S., & Nam, J. (2020). TLGAN: document Text Localization using Generative Adversarial Nets.
- Kim, G., Hong, T., Yim, M., Nam, J., Park, J., Yim, J., Hwang, W., Yun, S., Han, D., & Park, S. (2022). OCR-Free Document Understanding Transformer (pp. 498-517). https://doi.org/10.1007/978-3-031-19815-1_29
- Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. https://doi.org/10.1145/3065386
- Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016). Neural Architectures for Named Entity Recognition.
- Landau, H. J. (1967). Sampling, data transmission, and the Nyquist rate. Proceedings of the IEEE, 55(10), 1701-1706. https://doi.org/10.1109/PROC.1967.5962
- Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. https://doi.org/10.1109/5.726791
- Li Deng. (2012). The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]. IEEE Signal Processing Magazine, 29(6), 141-142. https://doi.org/10.1109/MSP.2012.2211477
- Li, J., Xu, Y., Lv, T., Cui, L., Zhang, C., & Wei, F. (2022). DiT: Self-supervised Pre-training for Document Image Transformer.
- Liu, X., Shen, Y., Duh, K., & Gao, J. (2018). Stochastic Answer Networks for Machine Reading Comprehension. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1694-1704. https://doi.org/10.18653/v1/P18-1157
- Luo, X., Han, Z., Yang, L., & Zhang, L. (2022). Consistent Style Transfer. ArXiv:2201.02233 (2022).
- Luong, M.-T., Pham, H., & Manning, C. D. (2015). Effective Approaches to Attention-based Neural Machine Translation.
- Mori, S., Suen, C. Y., & Yamamoto, K. (1992). Historical review of OCR research and development. Proceedings of the IEEE, 80(7), 1029-1058. https://doi.org/10.1109/5.156468
- Ramadoni, Utami, E., & Fatta, H. al. (2021). Analysis on the Use of Declarative and Pull-based Deployment Models on GitOps Using Argo CD. 2021 4th International Conference on Information and Communications Technology (ICOIACT), 186-191. https://doi.org/10.1109/ICOIACT53268.2021.9563984
- Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779-788. https://doi.org/10.1109/CVPR.2016.91
- Riazi, M. S., Darvish Rouani, B., & Koushanfar, F. (2019). Deep Learning on Private Data. IEEE Security & Privacy, 17(6), 54-63. https://doi.org/10.1109/MSEC.2019.2935666
- Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation (pp. 234-241). https://doi.org/10.1007/978-3-319-24574-4_28
- Shelhamer, E., Long, J., & Darrell, T. (2017). Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640-651. https://doi.org/10.1109/TPAMI.2016.2572683
- Sherstinsky, A. (2020). Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306. https://doi.org/10.1016/j.physd.2019.132306
- Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition.
- Singh, A., Bacchuwar, K., & Bhasin, A. (2012). A Survey of OCR Applications. International Journal of Machine Learning and Computing, 314-318. https://doi.org/10.7763/IJMLC.2012.V2.137
- Subramani, N., Matton, A., Greaves, M., & Lam, A. (2020). A Survey of Deep Learning Approaches for OCR and Document Understanding.
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All You Need.
- xie, xuemei, Cao, G., Yang, W., Liao, Q., Shi, G., & Wu, J. (2018). Feature-fused SSD: fast detection for small objects. In H. Yu & J. Dong (Eds.), Ninth International Conference on Graphic and Image Processing (ICGIP 2017) (p. 236). SPIE. https://doi.org/10.1117/12.2304811
- Xu, Y., Li, M., Cui, L., Huang, S., Wei, F., & Zhou, M. (2020). LayoutLM: Pre-training of Text and Layout for Document Image Understanding. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1192-1200. https://doi.org/10.1145/3394486.3403172
- Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. 2017 IEEE International Conference on Computer Vision (ICCV), 2242-2251. https://doi.org/10.1109/ICCV.2017.244