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feat: Layout model specification and multiple choices (#1910)

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* Establish layout_model spec and example instantations

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>

* Updated naming

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>

* Back to uppercase constants

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>

* fix deps issue with openai-whipser>numba>llvmlite

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>

* Pull v1 changed test GT from main

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>

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Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
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Christoph Auer
2025-07-10 06:37:27 +02:00
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tests/data/groundtruth/docling_v2/2203.01017v2.md vendored
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@@ -322,7 +322,7 @@ Computer Vision and Pattern Recognition , pages 658-666, 2019. 6
- [35] Quanzeng You, Hailin Jin, Zhaowen Wang, Chen Fang, and Jiebo Luo. Image captioning with semantic attention. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 4651-4659, 2016. 4
- [36] Xinyi Zheng, Doug Burdick, Lucian Popa, Peter Zhong, and Nancy Xin Ru Wang. Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. Winter Conference for Applications in Computer Vision (WACV) , 2021. 2, 3
- [37] Xu Zhong, Elaheh ShafieiBavani, and Antonio Jimeno Yepes. Image-based table recognition: Data, model,
- and evaluation. In Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm, editors, Computer Vision ECCV 2020 , pages 564-580, Cham, 2020. Springer International Publishing. 2, 3, 7
13. and evaluation. In Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm, editors, Computer Vision ECCV 2020 , pages 564-580, Cham, 2020. Springer International Publishing. 2, 3, 7
- [38] Xu Zhong, Jianbin Tang, and Antonio Jimeno Yepes. Publaynet: Largest dataset ever for document layout analysis. In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1
## TableFormer: Table Structure Understanding with Transformers Supplementary Material
@@ -369,7 +369,7 @@ Here is a step-by-step description of the prediction postprocessing:
1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure.
2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches.
3. Use a carefully selected IOU threshold to designate the matches as "good" ones and "bad" ones.
- 3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.
4. 3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column.
4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula:
<!-- formula-not-decoded -->