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"text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."
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"text": "Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables."
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"text": "Identifying the structure of a table has been an outstand-",
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"text": "ing problem in the document-parsing community, that mo-",
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"text": "tivates many organised public challenges [6, 4, 14].",
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"text": "difficulty of the problem can be attributed to a number of",
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"text": "of tables.",
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"text": "Such large variety requires a flexible method.",
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"text": "This is especially true for complex column- and row head-",
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"text": "ers, which can be extremely intricate and demanding.",
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"text": "second factor of complexity is the lack of data with regard",
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"text": "to table-structure. Until the publication of PubTabNet [37],",
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"text": "there were no large datasets (i.e.",
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"text": "K tables) that pro-",
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"text": "vided structure information. This happens primarily due to",
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"text": "the fact that tables are notoriously time-consuming to an-",
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"text": "cell. The network architecture of IEDD is certainly more",
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"text": "elaborate, but it has the advantage that one can pre-train the",
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"text": "ilar to network architectures that try to provide captions to",
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"text": "Hybrid Deep Learning-Rule-Based approach",
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"text": ": A pop-",
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"text": "ular current model for table-structure identification is the",
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"text": "use of a hybrid Deep Learning-Rule-Based approach similar",
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"text": "to [27, 29]. In this approach, one first detects the position of",
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"text": "represent the table-structure. With this approach, one can",
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"text": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."
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"text": "ular current model for table-structure identification is the",
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"text": "its table-structure. Currently, this approach achieves state-",
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"text": "of-the-art results, but is not an end-to-end deep-learning",
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"text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."
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"text": "3. Datasets"
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"text": "We rely on large-scale datasets such as PubTabNet [37],",
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"text": "FinTabNet [36], and TableBank [17] datasets to train and",
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"text": "Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]."
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"text": "Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered."
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"text": ") to be scale invariant as explained in [25]. In",
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"text": ") to be scale invariant as explained in [25]. In",
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"text": "generation mechanism (see Sec.",
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"text": ") to be scale invariant as explained in [25]. In",
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"text": "ground-truth boxes, as we have already achieved a one-to-",
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"text": "one match through two steps: 1) Our token input sequence",
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"text": "In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce \"SynthTabNet\" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets."
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"text": "Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas",
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"text": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-"
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"text": "ing FinTabNet, 68% of the simple and 98% of the complex",
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"text": "spectrum of table data we have synthesized four types of",
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"text": "ances in regard to their size, structure, style and content.",
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"text": "1.1. Data preparation"
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"text": "avoid. As such, we prefer to have \u201dstrict\u201d tables, i.e. tables",
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"text": "As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have \"strict\" tables, i.e. tables where every row has exactly the same length."
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"text": "ing FinTabNet, 68% of the simple and 98% of the complex",
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"text": "We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes."
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"text": "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."
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"text": "3. Use a carefully selected IOU threshold to designate the matches as \"good\" ones and \"bad\" ones."
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"text": "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:"
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"text": "we must de-duplicate the columns according to highest to-",
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"text": "8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score."
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"text": "Pick up the remaining orphan cells. There could be",
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},
{
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"text": "cases, when after applying all the previous post-processing",
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"text": "steps, some PDF cells could still remain without any match",
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"text": "to predicted cells.",
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"text": "However, it is still possible to deduce",
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"text": "the correct matching for an orphan PDF cell by mapping its",
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"text": "bounding box on the geometry of the grid. This mapping",
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"text": "decides if the content of the orphan cell will be appended to",
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"text": "an already matched table cell, or a new table cell should be",
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"text": "9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row)."
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"text": "9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row."
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"text": "9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column)."
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"text": "9d. Intersect the orphan\u2019s bounding box with the column",
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"confidence": 1.0,
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"text": "9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column."
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"text": "9e. If the table cell under the identified row and column",
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"text": "9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or-"
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