mirror of
https://github.com/DS4SD/docling.git
synced 2025-12-08 20:58:11 +00:00
feat: Support AsciiDoc and Markdown input format (#168)
* updated the base-model and added the asciidoc_backend Signed-off-by: Peter Staar <taa@zurich.ibm.com> * updated the asciidoc backend Signed-off-by: Peter Staar <taa@zurich.ibm.com> * Ensure all models work only on valid pages (#158) Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * ci: run ci also on forks (#160) --------- Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> Signed-off-by: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com> * fix: fix legacy doc ref (#162) Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com> * docs: typo fix (#155) * Docs: Typo fix - Corrected spelling of invidual to automatic Signed-off-by: ABHISHEK FADAKE <31249309+fadkeabhi@users.noreply.github.com> * add synchronize event for forks Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> --------- Signed-off-by: ABHISHEK FADAKE <31249309+fadkeabhi@users.noreply.github.com> Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> Co-authored-by: Michele Dolfi <dol@zurich.ibm.com> * feat: add coverage_threshold to skip OCR for small images (#161) * feat: add coverage_threshold to skip OCR for small images Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * filter individual boxes Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * rename option Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> --------- Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * chore: bump version to 2.1.0 [skip ci] * adding tests for asciidocs Signed-off-by: Peter Staar <taa@zurich.ibm.com> * first working asciidoc parser Signed-off-by: Peter Staar <taa@zurich.ibm.com> * reformatted the code Signed-off-by: Peter Staar <taa@zurich.ibm.com> * fixed the mypy Signed-off-by: Peter Staar <taa@zurich.ibm.com> * adding test_02.asciidoc Signed-off-by: Peter Staar <taa@zurich.ibm.com> * Drafting Markdown backend via Marko library Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * work in progress on MD backend Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * md_backend produces docling document with headers, paragraphs, lists Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Improvements in md parsing Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Detecting and assembling tables in markdown in temporary buffers Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Added initial docling table support to md_backend Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Cleaned code, improved logging for MD Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Fixes MyPy requirements, and rest of pre-commit Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Fixed example run_md, added origin info to md_backend Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * working on asciidocs, struggling with ImageRef Signed-off-by: Peter Staar <taa@zurich.ibm.com> * able to parse the captions and image uri's Signed-off-by: Peter Staar <taa@zurich.ibm.com> * fixed the mypy Signed-off-by: Peter Staar <taa@zurich.ibm.com> * Update all backends with proper filename in DocumentOrigin Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Update to docling-core v2.1.0 Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Fixes for MD Backend, to avoid duplicated text inserts into docling doc Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Fix styling Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Added support for code blocks and fenced code in MD Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * cleaned prints Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Added proper processing of in-line textual elements for MD backend Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Fixed issues with duplicated paragraphs and incorrect lists in pptx Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Fixed issue with group ordeering in pptx backend, added gebug log into run with formats Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> --------- Signed-off-by: Peter Staar <taa@zurich.ibm.com> Signed-off-by: Christoph Auer <cau@zurich.ibm.com> Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> Signed-off-by: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com> Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com> Signed-off-by: ABHISHEK FADAKE <31249309+fadkeabhi@users.noreply.github.com> Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> Co-authored-by: Peter Staar <taa@zurich.ibm.com> Co-authored-by: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com> Co-authored-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com> Co-authored-by: ABHISHEK FADAKE <31249309+fadkeabhi@users.noreply.github.com> Co-authored-by: Michele Dolfi <dol@zurich.ibm.com> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Maksym Lysak <mly@zurich.ibm.com>
This commit is contained in:
Christoph Auer
GitHub
@@ -16,7 +16,6 @@ In modern document understanding systems [1,15], table extraction is typically a
|
||||
|
||||
Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL).
|
||||
|
||||
|
||||
<!-- image -->
|
||||
|
||||
today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22].
|
||||
@@ -47,7 +46,6 @@ ulary and can be interpreted as a table structure. For example, with the HTML to
|
||||
|
||||
Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet.
|
||||
|
||||
|
||||
<!-- image -->
|
||||
|
||||
Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure.
|
||||
@@ -70,40 +68,31 @@ In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines
|
||||
|
||||
The OTSL vocabulary is comprised of the following tokens:
|
||||
|
||||
-"C" cell a new table cell that either has or does not have cell content
|
||||
|
||||
-"L" cell left-looking cell , merging with the left neighbor cell to create a span
|
||||
|
||||
-"U" cell up-looking cell , merging with the upper neighbor cell to create a span
|
||||
|
||||
-"X" cell cross cell , to merge with both left and upper neighbor cells
|
||||
|
||||
-"NL" new-line , switch to the next row.
|
||||
- -"C" cell a new table cell that either has or does not have cell content
|
||||
- -"L" cell left-looking cell , merging with the left neighbor cell to create a span
|
||||
- -"U" cell up-looking cell , merging with the upper neighbor cell to create a span
|
||||
- -"X" cell cross cell , to merge with both left and upper neighbor cells
|
||||
- -"NL" new-line , switch to the next row.
|
||||
|
||||
A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML.
|
||||
|
||||
Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding
|
||||
|
||||
|
||||
<!-- image -->
|
||||
|
||||
## 4.2 Language Syntax
|
||||
|
||||
The OTSL representation follows these syntax rules:
|
||||
|
||||
1. Left-looking cell rule : The left neighbour of an "L" cell must be either another "L" cell or a "C" cell.
|
||||
|
||||
2. Up-looking cell rule : The upper neighbour of a "U" cell must be either another "U" cell or a "C" cell.
|
||||
- 1. Left-looking cell rule : The left neighbour of an "L" cell must be either another "L" cell or a "C" cell.
|
||||
- 2. Up-looking cell rule : The upper neighbour of a "U" cell must be either another "U" cell or a "C" cell.
|
||||
|
||||
## 3. Cross cell rule :
|
||||
|
||||
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.
|
||||
|
||||
4. First row rule : Only "L" cells and "C" cells are allowed in the first row.
|
||||
|
||||
5. First column rule : Only "U" cells and "C" cells are allowed in the first column.
|
||||
|
||||
6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with "NL" token.
|
||||
- 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.
|
||||
- 4. First row rule : Only "L" cells and "C" cells are allowed in the first row.
|
||||
- 5. First column rule : Only "U" cells and "C" cells are allowed in the first column.
|
||||
- 6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with "NL" token.
|
||||
|
||||
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.
|
||||
|
||||
@@ -121,7 +110,6 @@ To evaluate the impact of OTSL on prediction accuracy and inference times, we co
|
||||
|
||||
Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach.
|
||||
|
||||
|
||||
<!-- image -->
|
||||
|
||||
We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in
|
||||
@@ -134,7 +122,6 @@ We have chosen the PubTabNet data set to perform HPO, since it includes a highly
|
||||
|
||||
Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart.
|
||||
|
||||
|
||||
| # | # | Language | TEDs | TEDs | TEDs | mAP | Inference |
|
||||
|------------|------------|------------|-------------|-------------|-------------|-------------|-------------|
|
||||
| enc-layers | dec-layers | Language | simple | complex | all | (0.75) | time (secs) |
|
||||
@@ -152,7 +139,6 @@ Additionally, the results show that OTSL has an advantage over HTML when applied
|
||||
|
||||
Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8).
|
||||
|
||||
|
||||
| | Language | TEDs | TEDs | TEDs | mAP(0.75) | Inference time (secs) |
|
||||
|--------------|------------|--------|---------|--------|-------------|-------------------------|
|
||||
| | Language | simple | complex | all | mAP(0.75) | Inference time (secs) |
|
||||
@@ -169,7 +155,6 @@ To illustrate the qualitative differences between OTSL and HTML, Figure 5 demons
|
||||
|
||||
Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). "PMC2807444_006_00.png" PubTabNet. μ
|
||||
|
||||
|
||||
<!-- image -->
|
||||
|
||||
μ
|
||||
@@ -178,7 +163,6 @@ Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (
|
||||
|
||||
Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. "PMC5406406_003_01.png" PubTabNet.
|
||||
|
||||
|
||||
<!-- image -->
|
||||
|
||||
## 6 Conclusion
|
||||
@@ -191,48 +175,28 @@ Secondly, OTSL has more inherent structure and a significantly restricted vocabu
|
||||
|
||||
## References
|
||||
|
||||
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
|
||||
- 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
|
||||
- 2. Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition in the wild using transformer and identity matrix-based augmentation. In: Porwal, U., Fornés, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545561. Springer International Publishing, Cham (2022)
|
||||
- 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)
|
||||
- 4. Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 894-901. IEEE (2019)
|
||||
|
||||
2. Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition in the wild using transformer and identity matrix-based augmentation. In: Porwal, U., Fornés, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545561. Springer International Publishing, Cham (2022)
|
||||
- 5. Kayal, P., Anand, M., Desai, H., Singh, M.: Tables to latex: structure and content extraction from scientific tables. International Journal on Document Analysis and Recognition (IJDAR) pp. 1-10 (2022)
|
||||
- 6. Lee, E., Kwon, J., Yang, H., Park, J., Lee, S., Koo, H.I., Cho, N.I.: Table structure recognition based on grid shape graph. In: 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). pp. 18681873. IEEE (2022)
|
||||
- 7. Li, M., Cui, L., Huang, S., Wei, F., Zhou, M., Li, Z.: Tablebank: A benchmark dataset for table detection and recognition (2019)
|
||||
- 8. Livathinos, N., Berrospi, C., Lysak, M., Kuropiatnyk, V., Nassar, A., Carvalho, A., Dolfi, M., Auer, C., Dinkla, K., Staar, P.: Robust pdf document conversion using recurrent neural networks. Proceedings of the AAAI Conference on Artificial Intelligence 35 (17), 15137-15145 (May 2021), https://ojs.aaai.org/index.php/ AAAI/article/view/17777
|
||||
- 9. Nassar, A., Livathinos, N., Lysak, M., Staar, P.: Tableformer: Table structure understanding with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4614-4623 (June 2022)
|
||||
- 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
|
||||
- 11. Prasad, D., Gadpal, A., Kapadni, K., Visave, M., Sultanpure, K.: Cascadetabnet: An approach for end to end table detection and structure recognition from imagebased documents. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 572-573 (2020)
|
||||
- 12. Schreiber, S., Agne, S., Wolf, I., Dengel, A., Ahmed, S.: Deepdesrt: Deep learning for detection and structure recognition of tables in document images. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR). vol. 1, pp. 1162-1167. IEEE (2017)
|
||||
- 13. Siddiqui, S.A., Fateh, I.A., Rizvi, S.T.R., Dengel, A., Ahmed, S.: Deeptabstr: Deep learning based table structure recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1403-1409 (2019). https:// doi.org/10.1109/ICDAR.2019.00226
|
||||
- 14. Smock, B., Pesala, R., Abraham, R.: PubTables-1M: Towards comprehensive table extraction from unstructured documents. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4634-4642 (June 2022)
|
||||
- 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
|
||||
- 16. Wang, X.: Tabular Abstraction, Editing, and Formatting. Ph.D. thesis, CAN (1996), aAINN09397
|
||||
- 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)
|
||||
|
||||
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)
|
||||
|
||||
4. Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 894-901. IEEE (2019)
|
||||
|
||||
5. Kayal, P., Anand, M., Desai, H., Singh, M.: Tables to latex: structure and content extraction from scientific tables. International Journal on Document Analysis and Recognition (IJDAR) pp. 1-10 (2022)
|
||||
|
||||
6. Lee, E., Kwon, J., Yang, H., Park, J., Lee, S., Koo, H.I., Cho, N.I.: Table structure recognition based on grid shape graph. In: 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). pp. 18681873. IEEE (2022)
|
||||
|
||||
7. Li, M., Cui, L., Huang, S., Wei, F., Zhou, M., Li, Z.: Tablebank: A benchmark dataset for table detection and recognition (2019)
|
||||
|
||||
8. Livathinos, N., Berrospi, C., Lysak, M., Kuropiatnyk, V., Nassar, A., Carvalho, A., Dolfi, M., Auer, C., Dinkla, K., Staar, P.: Robust pdf document conversion using recurrent neural networks. Proceedings of the AAAI Conference on Artificial Intelligence 35 (17), 15137-15145 (May 2021), https://ojs.aaai.org/index.php/ AAAI/article/view/17777
|
||||
|
||||
9. Nassar, A., Livathinos, N., Lysak, M., Staar, P.: Tableformer: Table structure understanding with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4614-4623 (June 2022)
|
||||
|
||||
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
|
||||
|
||||
11. Prasad, D., Gadpal, A., Kapadni, K., Visave, M., Sultanpure, K.: Cascadetabnet: An approach for end to end table detection and structure recognition from imagebased documents. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 572-573 (2020)
|
||||
|
||||
12. Schreiber, S., Agne, S., Wolf, I., Dengel, A., Ahmed, S.: Deepdesrt: Deep learning for detection and structure recognition of tables in document images. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR). vol. 1, pp. 1162-1167. IEEE (2017)
|
||||
|
||||
13. Siddiqui, S.A., Fateh, I.A., Rizvi, S.T.R., Dengel, A., Ahmed, S.: Deeptabstr: Deep learning based table structure recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1403-1409 (2019). https:// doi.org/10.1109/ICDAR.2019.00226
|
||||
|
||||
14. Smock, B., Pesala, R., Abraham, R.: PubTables-1M: Towards comprehensive table extraction from unstructured documents. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4634-4642 (June 2022)
|
||||
|
||||
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
|
||||
|
||||
16. Wang, X.: Tabular Abstraction, Editing, and Formatting. Ph.D. thesis, CAN (1996), aAINN09397
|
||||
|
||||
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)
|
||||
|
||||
18. Xue, W., Yu, B., Wang, W., Tao, D., Li, Q.: Tgrnet: A table graph reconstruction network for table structure recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1295-1304 (2021)
|
||||
|
||||
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
|
||||
|
||||
20. Zhang, Z., Zhang, J., Du, J., Wang, F.: Split, embed and merge: An accurate table structure recognizer. Pattern Recognition 126 , 108565 (2022)
|
||||
|
||||
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
|
||||
|
||||
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)
|
||||
|
||||
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)
|
||||
- 18. Xue, W., Yu, B., Wang, W., Tao, D., Li, Q.: Tgrnet: A table graph reconstruction network for table structure recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1295-1304 (2021)
|
||||
- 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
|
||||
- 20. Zhang, Z., Zhang, J., Du, J., Wang, F.: Split, embed and merge: An accurate table structure recognizer. Pattern Recognition 126 , 108565 (2022)
|
||||
- 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
|
||||
- 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)
|
||||
- 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)
|
||||
Reference in New Issue
Block a user