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Tables are a common structuring element in many documents, such as PDF files. To reuse such tables, appropriate methods need to b e develop, which capture the structure and the content information. We have developed several heuristics which together recognize and decompose tables in PDF files and store the extracted data in a structured data format...
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... this procedure we have a table consisting of more than one column. For the first five columns of our example in Fig. 2 we get the resulting columns presented in Table. 1. Finally, we have to identify neighbor cells with the same content and merge them. In our case the four cells with the content ”Families having stock holding direct or indirect” are merged into one single cell with a column spanning of four. These are the main steps of our approach to extract table information from PDF files. Because of the complexity of the task and the used heuristics, which cannot cover all possible table structures, one cannot assume that the approach always returns correct results. For example, our approach cannot distinguish between hidden tables (i.e., tables that are not labeled as such in the original file) and real tables. Further, tables that are positioned vertically on a page cannot be captured. There are also several possible errors, for example, text chunks that do not belong together are merged, multi-line block objects that belong together are not merged, data cells are assigned to wrong columns, and so forth. It is also possible that areas that are not tables are identified as such. This is the case, for example, with bulleted lists, etc. To overcome these limitations we also implemented a graphical user interface which gives the user the ability of making adjustments on the extracted data. The user can make adjustments on cell level (e.g. delete cells, merge cells, edit content of cell, etc.) or on table level (e.g. delete table, merge tables, delete/insert rows or columns). The main limitation of the tool is that it is based on the results of the pdftohtml tool. If this tool returns wrong information or no information at all, our approach cannot be applied. For example, PDF files sometimes contain only the image of a table and not text chunks which are inserted by an author. In such a case, the pdftohtml returns no useful information. We stated this limitation as the main limitation, because the user cannot do anything about it. The graphical user interface will not help, either. The evaluation of an Information Extraction System is a non-trivial issue. Therefore, we can say that the MUCs’ scoring program represented an important first step in the right direction [6]. These conferences served as an evaluation platform where systems from different sites were evaluated by using different measures. Over the years the recall and precision measures established themselves and are widely accepted as a means for giving evidence about a system’s performance. Currently, some research goes in the direction of finding new and proper measures for evaluating table-processing approaches [7]. However, it is hard to predict how good a measure reflects the real situation for a current approach. Our approach, for example, consists of several iterative steps and a failure in the first step would affect the end result to an unpredictable extent. But it would be very hard to evaluate the performance of each heuristic separately. Thus, we decided to evaluate the end result using the mentioned most established measures in the IE community, namely the recall and precision measures [2]. We evaluated the table recognition and decomposition task separately and trans- formed the formula for recall and precision according to the tasks. The formula for the table recognition task is as ...
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... the appropriate column for a text object requires a heuristic itself, which is described by Algorithm 5. After all these processes we have a list of columns which together compose the tables. Now, the only thing to do is to merge cells with the same content to one cell with a greater spanning value. In the following we will give an example to illustrate several steps of our approach. Assume that we have as input the PDF file with a page like in Fig. 2. Of course, the PDF file contains not only the table but also text paragraphs, footnotes, etc., too. After getting the results from the pdftohtml tool we can go on with our approach. Our first step is to sort all the text elements in respect to their top attributes. Assume that we have already identified the text elements before the table and let us begin with the text elements in the table (refer Fig. 3). In Fig. 3, after the sorting process we have the following ordering: ”Median value among families”, ”Families having stock holdings”, ”with holdings”, ”direct or indirect”, ”Family”, ”(thousands of 1998 dollars)”, ”characteristic”, ”1989”, ”1992”, ”1995”, ”1998”, ”1989”, and so on. Now, Algorithm 1 is applied to create the line objects. Based on the ordering the first text element that is saved in a line object is ”Median value among families”. Thus, a new line object is created and the top and bottom values are actualized in respect to the added text element. The next one is ”Families having stock holdings” and we must look whether we can put this text in an existing line object or not. The first dashed line (see Fig. 3) marks the bottom of the line object we just created. As you can see the current text elements’ top value is between the top and the bottom value of our first line object and thus can be added to this line. After adding, the line objects’ top and bottom values are actualized. This procedure is applied until all text objects have been found in a line object (the last text object in our example is ”1989”). The text elements in this line object are still sorted according to their top values. This ordering is of no use anymore, because we want to gain the text chunks that semantically belong together. For example, we want ”Family” and ”characteristic” merged. Thus, we next sort the text elements in the line object according to their left values. After that we have the wanted ordering, thus ”Family characteristic”, ”Families having stock holdings direct or indirect”, and so on (refer Fig. 3). After building all line objects in this page we have to classify all line objects as single-line object or multi-line object. Algorithm 2 marks successive multi-line objects as multi-line block objects. Because we have no other multi-line block object in this example we do not have to merge anything (refer Algorithm 3). The next step is to create columns and assign the text objects to their corresponding columns. This step is done by Algorithm 4 and Algorithm 5. For our first text object in the first line object (”Family characteristic”) we have to build a new column. For all the text objects in the line objects we have to look whether there exists a column to which that text object can be assigned. If so, we simply add the text object to this column. If not, we create a new column and add this text object to the new one. In both cases, we actualize the columns’ horizontal boundaries according to the new added text element. A text object can be assigned to a column if one of the following four possibilities appears (refer Fig. ...
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Citations
... Unfortunately, many tables miss lines to separate some columns or rows and some techniques do not apply in these cases. Yildiz et al [149] present approaches based on line intervals and columns to identify the entities corresponding to tables' cells. Note that table extraction will be detailed in Section III-D. ...
... Reference Topic [133] seminal work on data extraction [7] computational-geometry algorithms for analyzing document structures [2] handling multiple types of data structures [146] considering relations between data [131] orientation of documents [16] document layout analysis [11] data sets for evaluation [81] seminal work on pdf documents management [48] data extraction from tables [113] table extraction for pdf documents [41] table detection for multipage pdf documents [24] solving of the maximum independent set of rectangles problem [149] pdf2table : method for extracting table [46] graph neural network for extracting tables from pdf documents [152] deep learning for pdf table extraction [104] presentation of TAO for table detection and extraction Reference Topic [85] seminal work on NER [135] NER Challenge at CoNLL [35] ACE program : challenge for NER systems [20] empirical study of NER [3] procedure to automatically extend an ontology with domain specific knowledge [40] system for NER in the open domain [90] model architectures for computing continuous vector representations of words (word2vec) [91] distributed architectures for word2vec [126] adaptation of word2vec to NER [73] neural networks for NER [27] neural networks for NER [4] bidirectional recurrent neural network for NER [34] presentation of BERT [54] combination of convolutional neural network with BERT [115] application of bert-cnn for an application in health care [105] presentation of ELMO, a model language word representation [36] use of ELMO for NER [116] Bert-cnn for speech identification [111] enhancing language comprehension through pre-training [43] data extraction from financial documents [130] state of the NER for French language [52] specific work on invoices [26] rule-based information extraction systems [103] information extraction from scanned invoices [5] constraint satisfaction for invoice processing ABBY a commercial system for NER ...
This paper provides a comprehensive overview of the process for information retrieval from invoices. Invoices serve as proof of purchase and contain important information, including the date, description, quantity, and the price of goods or services, as well as the terms of payment. Companies must process invoices quickly and accurately to maintain proper financial records. To automate this workflow, commercial systems have been developed. Despite the complexity involved, realizing automated processing of invoices necessitates the harmonious integration of a wide range of techniques and methods. While several surveys have shed light on different aspects of this workflow, our objective in this paper is to present a synthetic view of the process and emphasize the most pertinent challenges. We discuss the digitalization of invoices and the use of natural language processing techniques to extract relevant information. We also review machine learning and deep learning techniques that are widely used to handle the variability of layouts, minimize end-user tasks, and train and adapt to new contexts. The purpose of this overview is not to evaluate various systems and algorithms, but rather to propose a survey that reviews a wide scope of techniques for different data extraction tasks, addressing both information extraction and structure recognition for invoice processing. Specifically, we focus on table processing, paying particular attention to graph-based approaches.
... Several systems and frameworks are presented for extraction, processing, and understanding of tables from images, web, and PDF documents (Yildiz et al., 2005, Burdick et al., 2020, Colter et al., 2021, Kashinath et al., 2022, Ma et al., 2022. These approaches emphasize the extraction of tables and their explicit structural features, whereas, a recent study contends that table comprehension would benefit from including implicit contextual information (Shigarov 2022). ...
The ubiquitous and layout-friendly PDF documents have multiple elements for disseminating knowledge and require visual cues for interpretation. Whereas, users with visual impairments depend on assistive technologies which do not offer comprehensive information about non-textual elements such as tables. This emphasizes the importance of making PDF documents and their tables accessible to everyone. Hence, this study proposes to unveil the hidden semantics of PDF tables to blind and visually inspired people for a comprehensive understanding. Therefore, a heuristic approach is used to extract the explicit and implicit features including metadata, functional, structural, content, and contextual information. The extracted features are utilized to provide insights into the PDF table to the intended users by providing a lay summary before navigating the rows and columns. The proposed solution is evaluated quantitatively for the visual features of a table including captions, headers, and structures, and obtained encouraging results using precision, recall, and F-score. Additionally, qualitative evaluation is conducted by domain experts to assess the functionalities of the developed prototype against the developed heuristics and the adherence to standard accessibility guidelines of Web Content Accessibility Guidelines (WCAG 2.1) and ICT accessibility section 508. The extracted features can be utilized in various downstream applications, such as table classification, integration, searching, recommendation, and gerontechnology. By providing insights into PDF tables, this research serves as a starting point to improve accessibility for blind or visually impaired people.
... 1) Heuristic-based approach is to specify a set of rules and apply the rules on table structure recognition, such as PDF2TABLE [2], PDF-TREX [3] and an automatic table metadata extraction algorithm [4]. ...
Table structure recognition (TSR) is crucial for document analysis, particularly for medical examination report tables (MERTs), impacting efficiency and decision-making in healthcare. Most models for TSR utilize either Graph Convolution Neural Networks (GCNs) or Transformers with html sequences for structure recognition. These methods, however, face challenges with graph inductive bias and instability in training, respectively. We observe that cells within the same row or column of a table not only are closely aligned in their vertical and horizontal coordinates, respectively, but also exhibit highly similar features. In previous work, the spatial feature of coordinates was often only used for concatenation with image features, text features, etc.We believe that explicitly utilizing the unique spatial properties of tables can better encode table features. In this paper, we introduce a novel structure named Dual-Awareness Feature Aggregator (DAFA) for table, which leverages attention mechanisms to effectively extract table features. Based on it, we design an end-to-end model called DAFA-Net requiring only images as input, without the need for additional information such as texts. In addition, we try to address the prevalent challenge of recognizing cross-row and cross-column cells in TSR — a scenario frequently encountered in medical examination reports — by introducing a modified focal loss known as CRCC loss. We conduct extensive experiments on four popular datasets. This includes a dataset specifically dedicated to medical data and others that mirror the complexity typically encountered in medical tables. Experimental results show the effectiveness and potential of our DAFA-Net for TSR within the healthcare sector.
... Table structure recognition is the problem of identifying the structural layout of a table [27]. Yildiz et al. [37] proposed a technique for recognizing and reforming columns and rows by using the horizontal and vertical overlaps present between text blocks. Tensmeyer et al. [32] used dilated convolutions but depend upon heuristics approaches during postprocessing [13]. ...
Scientific documents contain tables that list important information in a concise fashion. Structure and content extraction from tables embedded within PDF research documents is a very challenging task due to the existence of visual features like spanning cells and content features like mathematical symbols and equations. Most existing table structure identification methods tend to ignore these academic writing features. In this paper, we adapt the transformer-based language modeling paradigm for scientific table structure and content extraction. Specifically, the proposed model converts a tabular image to its corresponding LaTeX source code. Overall, we outperform the current state-of-the-art baselines and achieve an exact match accuracy of 70.35 and 49.69% on table structure and content extraction, respectively. Further analysis demonstrates that the proposed models efficiently identify the number of rows and columns, the alphanumeric characters, the LaTeX tokens, and symbols.
... Table structure recognition is the problem of identifying the structural layout of a table [26]. Yildiz et al. [35] proposed a technique for recognizing and reforming columns and rows by using the horizontal and vertical overlaps present between text blocks. Tensmeyer et al. [31] used dilated convolutions but depend upon heuristics approaches during post-processing [12]. ...
Scientific documents contain tables that list important information in a concise fashion. Structure and content extraction from tables embedded within PDF research documents is a very challenging task due to the existence of visual features like spanning cells and content features like mathematical symbols and equations. Most existing table structure identification methods tend to ignore these academic writing features. In this paper, we adapt the transformer-based language modeling paradigm for scientific table structure and content extraction. Specifically, the proposed model converts a tabular image to its corresponding LaTeX source code. Overall, we outperform the current state-of-the-art baselines and achieve an exact match accuracy of 70.35 and 49.69% on table structure and content extraction, respectively. Further analysis demonstrates that the proposed models efficiently identify the number of rows and columns, the alphanumeric characters, the LaTeX tokens, and symbols.
... They firstly used rule-based methods to detect table structures and to locate tables using bounding-boxes. There have been lots of researches, such as the T-Res system presented by Kieninger [1], the PDF2table method proposed by Yildiz [2], and the method combined with OCR released by Tupaj et al. [3], etc. ...
Using deep learning networks to recognize the table attracts lots of attention. However, due to the lack of high-quality table datasets, the performance of using deep learning networks is limited. Therefore, TableRobot has been proposed, an automatic annotation method for heterogeneous tables. To be more specific, the annotations of table consist of the coordinates of the item block and the mapping relationship between item blocks and table cells. In order to transform the task, we successfully design an algorithm based on the greedy approach to find the optimum solution. To evaluate the performance of TableRobot, we check the annotation data of 3000 tables collected from the LaTex documents in arXiv.com , and the result shows that TableRobot can generate table annotation datasets with the accuracy of 93.2%. Besides, the table annotation data is feed into GraphTSR which is a state-of-the-art table recognition graph neural network, and the F1 value of the network has increased by nearly 10% compared with before.
... The T-Recs system proposed by Kieninger et al. used a bottom-up clustering approach to detect the word segments within the image and then combined them according to some predefined rules to obtain the conceptual text blocks (3). Yildiz et al. developed the pdf2table system, which employs multiple heuristics to identify tables in PDF files (4). Koci et al. adopted a graphic model to represent the layout and spatial features of the potential forms within a page and then identified the form as a subgraph using a genetic algorithm (5). ...
Background:
Complete electronic health records (EHRs) are not often available, because information barriers are caused by differences in the level of informatization and the type of the EHR system. Therefore, we aimed to develop a deep learning system [deep learning system for structured recognition of text images from unstructured paper-based medical reports (DeepSSR)] for structured recognition of text images from unstructured paper-based medical reports (UPBMRs) to help physicians solve the data-sharing problem.
Methods:
UPBMR images were firstly preprocessed through binarization, image correction, and image segmentation. Next, the table area was detected with a lightweight network (i.e., the proposed YOLOv3-MobileNet model). In addition, the text of the table area was detected and recognized with the model based on differentiable binarization (DB) and convolutional recurrent neural network (CRNN). Finally, the recognized text was structured according to its row and column coordinates. DeepSSR was trained and validated on our dataset with 4,221 UPBMR images which were randomly split into training, validation, and testing sets in a ratio of 8:1:1.
Results:
DeepSSR achieved a high accuracy of 91.10% and a speed of 0.668 s per image. In the system, the proposed YOLOv3-MobileNet model for table detection achieved a precision of 97.8% and a speed of 0.006 s per image.
Conclusions:
DeepSSR has high accuracy and fast speed in structured recognition of text based on UPBMR images. This system may help solve the data-sharing problem due to information barriers between hospitals with different EHR systems.
... This was done by detecting horizontal and vertical lines on the document to find the area enclosed by the lines and then using that area as the candidate area of the table. A few years later, Yildiz et al.[12] proposed a table detection method for pdf documents based on heuristic rules. The method first extracts text lines from the pdf and then merges them into a table according to the rules. ...
As financial document automation becomes more general, table detection is receiving more and more attention as an important part of document automation. Disclosure documents contain both bordered and borderless tables of varying lengths, and there is currently no model that performs well on these types of documents. To solve this problem, we propose a table detection model based on YOLO-table. We introduce involution into the backbone of the network to improve the network’s ability to learn table spatial layout features and design a simple Feature Pyramid Network to improve model effectiveness. In addition, this paper proposes a table-based augment method. We experiment on a disclosure document dataset, and the results show that the F1-measure of the YOLO-table reaches 97.3%. Compared with YOLOv3, our method improves the accuracy by 2.8% and the speed by 1.25 times. It also evaluates the ICDAR2013 and ICDAR2019 Table Competition datasets and achieves state-of-the-art performance.
... Table extraction (TE) [1,2] is the problem of inferring the presence, structure, and-to some extentmeaning of tables in documents or other unstructured presentations. In its presented form, a table is typically expressed as a collection of cells organized over a two-dimensional grid [3,4]. ...
In this paper, we propose a new class of evaluation metric for table structure recognition, grid table similarity (GriTS). Unlike prior metrics, GriTS evaluates the correctness of a predicted table directly in its natural form as a matrix. To create a similarity measure between matrices, we generalize the two-dimensional largest common substructure (2D-LCS) problem, which is NP-hard, to the 2D most similar substructures (2D-MSS) problem and propose a polynomial-time heuristic for solving it. We validate empirically using the PubTables-1M dataset that comparison between matrices exhibits more desirable behavior than alternatives for table structure recognition evaluation. GriTS also unifies all three subtasks of cell topology recognition, cell location recognition, and cell content recognition within the same framework, which simplifies the evaluation and enables more meaningful comparisons across different types of structure recognition approaches. Code will be released at https://github.com/microsoft/table-transformer.
... For PDF documents, heuristics-based approaches are popular and achieved promising performance. Typically, heuristics methods [26,34] need to define various rules and use the meta-data of the documents, meaning that this type of approach cannot process document images. Besides, the generalization ability of heuristics-based methods are often limited because of variations of table structures. ...
Tabular data in digital documents is widely used to express compact and important information for readers. However, it is challenging to parse tables from unstructured digital documents, such as PDFs and images, into machine-readable format because of the complexity of table structures and the missing of meta-information. Table Structure Recognition (TSR) problem aims to recognize the structure of a table and transform the unstructured tables into a structured and machine-readable format so that the tabular data can be further analysed by the down-stream tasks, such as semantic modeling and information retrieval. In this study, we hypothesize that a complicated table structure can be represented by a graph whose vertices and edges represent the cells and association between cells, respectively. Then we define the table structure recognition problem as a cell association classification problem and propose a conditional attention network (CATT-Net). The experimental results demonstrate the superiority of our proposed method over the state-of-the-art methods on various datasets. Besides, we investigate whether the alignment of a cell bounding box or a text-focused approach has more impact on the model performance. Due to the lack of public dataset annotations based on these two approaches, we further annotate the ICDAR2013 dataset providing both types of bounding boxes, which can be a new benchmark dataset for evaluating the methods in this field. Experimental results show that the alignment of a cell bounding box can help improve the Micro-averaged F1 score from 0.915 to 0.963, and the Macro-average F1 score from 0.787 to 0.923.
