# Referencias bibliográficas {.unnumbered}

Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna: A next-generation hyperparameter optimization framework. *Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining*, 2623-2631. https://doi.org/10.1145/3292500.3330701

Baek, Y., Lee, B., Han, D., Yun, S., & Lee, H. (2019). Character region awareness for text detection. *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*, 9365-9374. https://doi.org/10.1109/CVPR.2019.00959

Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. *Journal of Machine Learning Research*, 13(1), 281-305. https://jmlr.org/papers/v13/bergstra12a.html

Bergstra, J., Bardenet, R., Bengio, Y., & Kégl, B. (2011). Algorithms for hyper-parameter optimization. *Advances in Neural Information Processing Systems*, 24, 2546-2554. https://papers.nips.cc/paper/2011/hash/86e8f7ab32cfd12577bc2619bc635690-Abstract.html

Cohen, J. (1988). *Statistical power analysis for the behavioral sciences* (2nd ed.). Lawrence Erlbaum Associates.

Doran, G. T. (1981). There's a S.M.A.R.T. way to write management's goals and objectives. *Management Review*, 70(11), 35-36.

Du, Y., Li, C., Guo, R., Yin, X., Liu, W., Zhou, J., Bai, Y., Yu, Z., Yang, Y., Dang, Q., & Wang, H. (2020). PP-OCR: A practical ultra lightweight OCR system. *arXiv preprint arXiv:2009.09941*. https://arxiv.org/abs/2009.09941

Du, Y., Li, C., Guo, R., Cui, C., Liu, W., Zhou, J., Lu, B., Yang, Y., Liu, Q., Hu, X., Yu, D., & Wang, H. (2023). PP-OCRv4: Mobile scene text detection and recognition. *arXiv preprint arXiv:2310.05930*. https://arxiv.org/abs/2310.05930

Feurer, M., & Hutter, F. (2019). Hyperparameter optimization. In F. Hutter, L. Kotthoff, & J. Vanschoren (Eds.), *Automated machine learning: Methods, systems, challenges* (pp. 3-33). Springer. https://doi.org/10.1007/978-3-030-05318-5_1

He, P., Huang, W., Qiao, Y., Loy, C. C., & Tang, X. (2016). Reading scene text in deep convolutional sequences. *Proceedings of the AAAI Conference on Artificial Intelligence*, 30(1), 3501-3508. https://doi.org/10.1609/aaai.v30i1.10291

JaidedAI. (2020). EasyOCR: Ready-to-use OCR with 80+ supported languages. GitHub. https://github.com/JaidedAI/EasyOCR

Liang, J., Doermann, D., & Li, H. (2005). Camera-based analysis of text and documents: A survey. *International Journal of Document Analysis and Recognition*, 7(2), 84-104. https://doi.org/10.1007/s10032-004-0138-z

Liao, M., Wan, Z., Yao, C., Chen, K., & Bai, X. (2020). Real-time scene text detection with differentiable binarization. *Proceedings of the AAAI Conference on Artificial Intelligence*, 34(07), 11474-11481. https://doi.org/10.1609/aaai.v34i07.6812

Liaw, R., Liang, E., Nishihara, R., Moritz, P., Gonzalez, J. E., & Stoica, I. (2018). Tune: A research platform for distributed model selection and training. *arXiv preprint arXiv:1807.05118*. https://arxiv.org/abs/1807.05118

Mindee. (2021). DocTR: Document Text Recognition. GitHub. https://github.com/mindee/doctr

Moritz, P., Nishihara, R., Wang, S., Tumanov, A., Liaw, R., Liang, E., Elibol, M., Yang, Z., Paul, W., Jordan, M. I., & Stoica, I. (2018). Ray: A distributed framework for emerging AI applications. *13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18)*, 561-577. https://www.usenix.org/conference/osdi18/presentation/moritz

Morris, A. C., Maier, V., & Green, P. D. (2004). From WER and RIL to MER and WIL: Improved evaluation measures for connected speech recognition. *Eighth International Conference on Spoken Language Processing*. https://doi.org/10.21437/Interspeech.2004-668

PaddlePaddle. (2024). PaddleOCR: Awesome multilingual OCR toolkits based on PaddlePaddle. GitHub. https://github.com/PaddlePaddle/PaddleOCR

Pearson, K. (1895). Notes on regression and inheritance in the case of two parents. *Proceedings of the Royal Society of London*, 58, 240-242. https://doi.org/10.1098/rspl.1895.0041

PyMuPDF. (2024). PyMuPDF documentation. https://pymupdf.readthedocs.io/

Shi, B., Bai, X., & Yao, C. (2016). An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 39(11), 2298-2304. https://doi.org/10.1109/TPAMI.2016.2646371

Smith, R. (2007). An overview of the Tesseract OCR engine. *Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)*, 2, 629-633. https://doi.org/10.1109/ICDAR.2007.4376991

Zhou, X., Yao, C., Wen, H., Wang, Y., Zhou, S., He, W., & Liang, J. (2017). EAST: An efficient and accurate scene text detector. *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, 5551-5560. https://doi.org/10.1109/CVPR.2017.283

Zoph, B., & Le, Q. V. (2017). Neural architecture search with reinforcement learning. *International Conference on Learning Representations (ICLR)*. https://arxiv.org/abs/1611.01578

