feat: Add DoclingParseV4 backend, using high-level docling-parse API (#905)

* Add DoclingParseV3 backend implementation

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

* Use docling-core with docling-parse types

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

* Fixes and test updates

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

* Fix streams

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

* Fix streams

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

* Reset tests

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

* update test cases

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

* update test units

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

* Add back DoclingParse v1 backend, pipeline options

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

* Update locks

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

* fix: update docling-core to 2.22.0

Update dependency library docling-core to latest release 2.22.0
Fix regression tests and ground truth files

Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com>

* Ground-truth files updated

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

* Update tests, use TextCell.from_ocr property

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

* Text fixes, new test data

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

* Rename docling backend to v4

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

* Test all backends, fixes

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

* Reset all tests to use docling-parse v1 for now

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

* Fixes for DPv4 backend init, better test coverage

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

* test_input_doc use default backend

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

---------

Signed-off-by: Christoph Auer <cau@zurich.ibm.com>
Signed-off-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com>
Co-authored-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com>

tests/data/groundtruth/docling_v2/2203.01017v2.md

@@ -140,13 +140,13 @@ tention encoding is then multiplied to the encoded image to produce a feature fo
 
 The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer.
 
-Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$_{box}$ . l$_{box}$ consists of the generally used l$_{1}$ loss for object detection and the IoU loss ( l$_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets.
+Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l$\_{s}$ ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l$\_{box}$ . l$\_{box}$ consists of the generally used l$\_{1}$ loss for object detection and the IoU loss ( l$\_{iou}$ ) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets.
 
 The loss used to train the TableFormer can be defined as following:
 
 <!-- formula-not-decoded -->
 
-where λ ∈ [0, 1], and λ$_{iou}$, λ$_{l}$$\_{1}$ ∈$\_{R}$ are hyper-parameters.
+where λ ∈ [0, 1], and λ$\_{iou}$, λ$\_{l}$$\_{1}$ ∈$\_{R}$ are hyper-parameters.
 
 ## 5. Experimental Results
 
@@ -176,7 +176,7 @@ The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It
 
 <!-- formula-not-decoded -->
 
-where T$_{a}$ and T$_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .
+where T$\_{a}$ and T$\_{b}$ represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and | T | represents the number of nodes in T .
 
 ## 5.4. Quantitative Analysis
 
@@ -372,7 +372,7 @@ Here is a step-by-step description of the prediction postprocessing:
 
 <!-- formula-not-decoded -->
 
-where c is one of { left, centroid, right } and x$_{c}$ is the xcoordinate for the corresponding point.
+where c is one of { left, centroid, right } and x$\_{c}$ is the xcoordinate for the corresponding point.
 
 - 5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me-
 - 6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes.