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feat: leverage new list modeling, capture default markers (#1856)
* chore: update docling-core & regenerate test data Signed-off-by: Panos Vagenas <pva@zurich.ibm.com> * update backends to leverage new list modeling Signed-off-by: Panos Vagenas <pva@zurich.ibm.com> * repin docling-core Signed-off-by: Panos Vagenas <pva@zurich.ibm.com> * ensure availability of latest docling-core API Signed-off-by: Panos Vagenas <pva@zurich.ibm.com> --------- Signed-off-by: Panos Vagenas <pva@zurich.ibm.com>
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@@ -70,15 +70,15 @@ In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines
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The OTSL vocabulary is comprised of the following tokens:
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- -"C" cell a new table cell that either has or does not have cell content
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-"C" cell a new table cell that either has or does not have cell content
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- -"L" cell left-looking cell , merging with the left neighbor cell to create a span
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-"L" cell left-looking cell , merging with the left neighbor cell to create a span
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- -"U" cell up-looking cell , merging with the upper neighbor cell to create a span
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-"U" cell up-looking cell , merging with the upper neighbor cell to create a span
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- -"X" cell cross cell , to merge with both left and upper neighbor cells
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-"X" cell cross cell , to merge with both left and upper neighbor cells
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- -"NL" new-line , switch to the next row.
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-"NL" new-line , switch to the next row.
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A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML.
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@@ -89,19 +89,19 @@ Fig. 3. OTSL description of table structure: A - table example; B - graphical re
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The OTSL representation follows these syntax rules:
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- 1. Left-looking cell rule : The left neighbour of an "L" cell must be either another "L" cell or a "C" cell.
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1. Left-looking cell rule : The left neighbour of an "L" cell must be either another "L" cell or a "C" cell.
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- 2. Up-looking cell rule : The upper neighbour of a "U" cell must be either another "U" cell or a "C" cell.
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2. Up-looking cell rule : The upper neighbour of a "U" cell must be either another "U" cell or a "C" cell.
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## 3. Cross cell rule :
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- The left neighbour of an "X" cell must be either another "X" cell or a "U" cell, and the upper neighbour of an "X" cell must be either another "X" cell or an "L" cell.
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The left neighbour of an "X" cell must be either another "X" cell or a "U" cell, and the upper neighbour of an "X" cell must be either another "X" cell or an "L" cell.
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- 4. First row rule : Only "L" cells and "C" cells are allowed in the first row.
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4. First row rule : Only "L" cells and "C" cells are allowed in the first row.
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- 5. First column rule : Only "U" cells and "C" cells are allowed in the first column.
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5. First column rule : Only "U" cells and "C" cells are allowed in the first column.
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- 6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with "NL" token.
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6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with "NL" token.
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The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a "C"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid.
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@@ -177,48 +177,48 @@ Secondly, OTSL has more inherent structure and a significantly restricted vocabu
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## References
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1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785
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3. Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table structure recognition. arXiv preprint arXiv:1908.04729 (2019)
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10. Pfitzmann, B., Auer, C., Dolfi, M., Nassar, A.S., Staar, P.W.J.: Doclaynet: A large human-annotated dataset for document-layout segmentation. In: Zhang, A., Rangwala, H. (eds.) KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022. pp. 3743-3751. ACM (2022). https://doi.org/10.1145/3534678.3539043 , https:// doi.org/10.1145/3534678.3539043
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15. Staar, P.W.J., Dolfi, M., Auer, C., Bekas, C.: Corpus conversion service: A machine learning platform to ingest documents at scale. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 774-782. KDD '18, Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3219819.3219834 , https://doi.org/10. 1145/3219819.3219834
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16. Wang, X.: Tabular Abstraction, Editing, and Formatting. Ph.D. thesis, CAN (1996), aAINN09397
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17. Xue, W., Li, Q., Tao, D.: Res2tim: Reconstruct syntactic structures from table images. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 749-755. IEEE (2019)
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19. Ye, J., Qi, X., He, Y., Chen, Y., Gu, D., Gao, P., Xiao, R.: Pingan-vcgroup's solution for icdar 2021 competition on scientific literature parsing task b: Table recognition to html (2021). https://doi.org/10.48550/ARXIV.2105.01848 , https://arxiv.org/abs/2105.01848
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20. Zhang, Z., Zhang, J., Du, J., Wang, F.: Split, embed and merge: An accurate table structure recognizer. Pattern Recognition 126 , 108565 (2022)
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- 21. Zheng, X., Burdick, D., Popa, L., Zhong, X., Wang, N.X.R.: Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 697-706 (2021). https://doi.org/10.1109/WACV48630.2021. 00074
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21. Zheng, X., Burdick, D., Popa, L., Zhong, X., Wang, N.X.R.: Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 697-706 (2021). https://doi.org/10.1109/WACV48630.2021. 00074
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- 22. Zhong, X., ShafieiBavani, E., Jimeno Yepes, A.: Image-based table recognition: Data, model, and evaluation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision - ECCV 2020. pp. 564-580. Springer International Publishing, Cham (2020)
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22. Zhong, X., ShafieiBavani, E., Jimeno Yepes, A.: Image-based table recognition: Data, model, and evaluation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision - ECCV 2020. pp. 564-580. Springer International Publishing, Cham (2020)
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- 23. Zhong, X., Tang, J., Yepes, A.J.: Publaynet: largest dataset ever for document layout analysis. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1015-1022. IEEE (2019)
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23. Zhong, X., Tang, J., Yepes, A.J.: Publaynet: largest dataset ever for document layout analysis. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1015-1022. IEEE (2019)
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