Schema-Aware Multimodal Parsing of Complex Geoscience Documents

References

1. Castro, D., Paquet, A., and Besançon, R. [2022] Doctr: Document Text Recognition. Available at: https://mindee.github.io/doctr/ (Accessed: 13 October 2025).
   [Google Scholar]
2. Dong, T., Clarke, F., and Hou, S., [2025], Enhanced workflow for processing and gaining insights from tabular data. EAGE Conference Proceedings, 2025(1), 1–4.
   [Google Scholar]
3. Feng, H., Wei, S., Fei, X., Shi, W., Han, Y., Liao, L., Lu, J., Wu, B., Liu, Q., Lin, C., Tang, J., Liu, H. & Huang, C. [2025] Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting. arXiv preprint, arXiv:2505.14059
   [Google Scholar]
4. Hou, S., Dong, T., Sancheti, O., and Liu, H. [2025], Advancing geologic document digitalization and information retrieval with generative AI. The Leading Edge.
   [Google Scholar]
5. Li, J., Xu, Y., Lv, T., Cui, L., Zhang, C. and Wei, F. [2022] DiT: Self-supervised pre-training for Document Image Transformer. arXiv preprint, arXiv:2203.02378.
   [Google Scholar]
6. Lun, C.H., Hewitt, T., and Hou, S., [2022], A machine learning pipeline for document extraction: First Break, 40(2), 73–78.
   [Google Scholar]
7. Smock, B., Pesala, R., and Abraham, R. [2022] PubTables-1M: Towards comprehensive table extraction from unstructured documents. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4634–4642.
   [Google Scholar]