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VertexNet: a table structure recognition model based on key points for complex scenes

INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND recognition 2025年 1-15页

作者： Hong, Tao Yang, Yilun China Jiliang Univ Coll Qual & Standardizat Hangzhou Peoples R China

table recognition is a critical component of intelligent document processing, where table structure recognition is the most critical part. It aims to understand the distribution and structure of cells within tables and convert table images into a computer-understandable format. Existing research typically relies on row-column segmentation methods to parse tables, which perform inadequately on table images from natural scenes with diverse backgrounds and types. Therefore, we propose the VertexNet model. We use keypoint detection to detect the center point of a cell, then determine the position of four vertices by regressing a series of attributes, and finally stitch the predicted cells into a complete table. Based on this direct regression out of cells, we propose a new post-processing method will get the logical coordinates of the cells. We achieve an F1 score of 86.9% on the complex scene dataset WTW and 79.4% on Teds, surpassing state-of-the-art methods and demonstrating higher accuracy than row-column segmentation approaches. Evaluation on the ICDAR2013 and ICDAR2019 datasets further showcases the model's advantages.

关键词： table structure recognition Deep learning Target detection Computer vision

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Enhancing Transformer-Based table structure recognition for Long tables 7th

Enhancing Transformer-Based Table Structure Recognition for ...

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7th Chinese Conference on Pattern recognition and Computer Vision

作者： Zhu, Ziyi Zhao, Wenqi Gao, Liangcai Peking Univ Inst Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9789819785100;9789819785117

Extensive researches have demonstrated the effectiveness of Image-to-Sequence (img2seq) approaches in table structure recognition (TSR) task. However, when dealing with long tables, these methods always suffer from the inherent limitations of their attention mechanism, which hinders the further progress of this technical route. Based on the repetitive nature of table body structures, we present a novel approach for compressing table HTML code, resulting in a significant reduction in the overall length of the HTML code by more than 65%. Additionally, to address the relatively complex structure of table heads and the dense arrangement of rows and cells in long tables, we integrate coverage information into the transformer decoder. Experiments show that our method achieves performance comparable to the state-of-the-art methods with a simple model structure. What's more, our method effectively mitigates the performance degradation commonly observed in img2seq methods when the length of the target sequence increases.

关键词： table structure recognition Encoder-Decoder Model Transformer

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Rethinking detection based table structure recognition for visually rich document images

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 269卷

作者： Xiao, Bin Simsek, Murat Kantarci, Burak Abu Alkheir, Ala Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada Lytica Inc 555 Legget Dr Ottawa ON K2K 2X3 Canada

Detection models have been extensively employed for the table structure recognition (TSR) task, aiming to convert table images into structured formats by detecting table components such as Columns and Rows. However, prevailing detection-based TSR models usually cannot perform well regarding cell-level metrics, such as TEDS, and the reasons hindering their performance are not thoroughly explored. Therefore, we first examine the underlying reasons impeding these models' performance and find that the key issues are the improper problem formulation, the mismatch issue of detection and TSR metrics, the inherent characteristics of detection models, and the influence of local and long-range feature extraction. Based on these findings, we propose a tailored Cascade R-CNN based solution by introducing anew problem formulation, tuning the proposal generation, and applying deformation convolution and the proposed Spatial Attention Module. The experimental results show that our proposed model can improve the base Cascade R-CNN model by 19.32%, 11.56%, and 14.77% on the SciTSR, FinTabNet, and Pubtables1M datasets regarding the structure-only TEDS, achieving state-of-the-art performance, demonstrating that our findings can serve as a valuable guide for enhancing detection-based TSR models. Our code and pre-trained models are public available1.

关键词： table structure recognition Information extraction Document processing Object detection Visually rich document understanding

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LORE++: Logical location regression network for table structure recognition with pre-training

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Pattern recognition 2025年 157卷

作者： Long, Rujiao Xing, Hangdi Yang, Zhibo Zheng, Qi Yu, Zhi Huang, Fei Yao, Cong Alibaba Group Zhejiang HangZhou China Zhejiang Provincial Key Laboratory of Service Robot Zhejiang University Zhejiang Hangzhou China Equal contribution.

table structure recognition (TSR) aims at extracting tables in images into machine-understandable formats. Current approaches address this issue by either predicting the adjacency of detected cells or direct generation of structural sequences. Nonetheless, these approaches either count on additional heuristic rules for post-processing, or involve the generation of extremely long-range sequences that lead to computational intricacy. In this paper, We redefine TSR as a LOgical location REgression paradigm, which effectively captures inherent logical dependencies and constraints among table cells. Correspondingly, we propose LORE, a novel approach for TSR. LORE simultaneously predicts accurate geometric coordinates of table cells and the logical structures of the entire table. Our proposed LORE is conceptually simpler, easier to train, and more accurate than other TSR paradigms. Moreover, to enhance the model's spatial and logical representation capabilities, we propose two pre-training tasks, resulting in an upgraded version named LORE++. The incorporation of pre-training is proven to enjoy significant advantages, leading to a substantial enhancement in terms of accuracy, generalization, and few-shot capability compared to its predecessor. Experiments on standard benchmarks demonstrate the superiority of LORE++, which highlights the potential and promising prospect of the logical location regression paradigm for TSR. © 2024 Elsevier Ltd

关键词： Visual document understanding table structure recognition OCR

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table structure recognition using black widow based mutual exclusion and RESNET attention model

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第1期46卷 1101-1114页

作者： Tiwari, Devendra Gupta, Anand Univ Coll Engn & Technol Bikaner India Netaji Subhas Univ Technol New Delhi India

tables are commonly used for effective and compact representation of relational information across the data in diverse document classes like scientific papers, financial statements, newspaper articles, invoices, or product descriptions. However, table structure detection is a relatively simple process for humans, but recognizing precise table structure is still a computer vision challenge. Further, innumerable possible table layouts increase the risk of automatic topic modeling and understanding the capability of each table from the generic document. This paper develops the framework to recognize the table structure from the Compound Document Image(CDI). Initially, the bilateral filter is designed for image transformation, enhancing CDI quality. An improved binarization-Sauvola algorithm (IBSA) is proposed to degrade the tables with uneven illumination, low contrast, and uniform background. The morphological Thinning method extracts the line from the table. The masking approach extracts the row and column from the table. Finally, the ResNet Attention model optimized over Black Widow optimization-based mutual exclusion (BWME) is developed to recognize the table structure from the document images. The UNLV, tableBank, and ICDAR-2013 table competition datasets are used to evaluate the proposed framework's performance. Precision and accuracy are the metrics considered for evaluating the proposed framework performance. From the experimental results, the proposed framework achieved a precision value of 96.62 and the accuracy value of 94.34, which shows the effectiveness of the proposed approach's performance.

关键词： Image transformation table extraction ResNet Attention model table structure recognition

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table structure recognition Using CoDec Encoder-Decoder 16th

Table Structure Recognition Using CoDec Encoder-Decoder

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16th IAPR International Conference on Document Analysis and recognition (ICDAR)

作者： Pegu, Bhanupriya Singh, Maneet Agarwal, Aakash Mitra, Aniruddha Singh, Karamjit Mastercard AI Garage Gurgaon India

ISBN: (纸本)9783030861599;9783030861582

Automated document analysis and parsing has been the focus of research since a long time. An important component of document parsing revolves around understanding tabular regions with respect to their structure identification, followed by precise information extraction. While substantial effort has gone into table detection and information extraction from documents, table structure recognition remains to be a long-standing task demanding dedicated attention. The identification of the table structure enables extraction of structured information from tabular regions which can then be utilized for further applications. To this effect, this research proposes a novel table structure recognition pipeline consisting of row identification and column identification modules. The column identification module utilizes a novel Column Detector Encoder-Decoder model (termed as CoDec Encoder Decoder) which is trained via a novel loss function for predicting the column mask for a given input image. Experiments have been performed to analyze the different components of the proposed pipeline, thus supporting their inclusion for enhanced performance. The proposed pipeline has been evaluated on the challenging ICDAR 2013 table structure recognition dataset, where it demonstrates state-of-the-art performance.

关键词： table structure recognition Encoder-Decoder Document analysis

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ClusTi: Clustering Method for table structure recognition in Scanned Images

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MOBILE NETWORKS & APPLICATIONS 2021年第4期26卷 1765-1776页

作者： Zucker, Arthur Belkada, Younes Hanh Vu Van Nam Nguyen Sorbonne Univ Polytech Sorbonne F-75005 Paris France Viettel CyberSpace Ctr 41st FloorKeangnam Landmark 72 Hanoi Vietnam Thuyloi Univ Comp Sci & Engn Dept 175 TaySon Hanoi Vietnam

OCR (Optical Character recognition) for scanned paper invoices is very challenging due to the variability of 19 invoice layouts, different information fields, large data tables, and low scanning quality. In this case, table structure recognition is a critical task in which all rows, columns, and cells must be accurately positioned and extracted. Existing methods such as DeepDeSRT only dealt with high-quality born-digital images (e.g., PDF) with low noise and apparent table structure. This paper proposes an efficient method called CluSTi (Clustering method for recognition of the structure of tables in invoice scanned Images). The contributions of CluSTi are three-fold. Firstly, it removes heavy noises in the table images using a clustering algorithm. Secondly, it extracts all text boxes using state-of-the-art text recognition. Thirdly, based on the horizontal and vertical clustering algorithm with optimized parameters, CluSTi groups the text boxes into their correct rows and columns, respectively. The method was evaluated on three datasets: i) 397 public scanned images;ii) 193 PDF document images from ICDAR 2013 competition dataset;and iii) 281 PDF document images from ICDAR 2019's numeric tables. The evaluation results showed that CluSTi achieved an F-1-score of 87.5%, 98.5%, and 94.5%, respectively. Our method also outperformed DeepDeSRT with an F-1-score of 91.44% on only 34 images from the ICDAR 2013 competition dataset. To the best of our knowledge, CluSTi is the first method to tackle the table structure recognition problem on scanned images.

关键词： table structure recognition Object recognition Clustering method

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Robust table structure recognition Network Based on Local and Global Perspectives

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IEEE ACCESS 2024年 12卷 140160-140170页

作者： Song, Liangliang Huang, Xiang Zhuang, Shuyi Shen, Aodong Xu, Peidong State Grid Jiangsu Elect Power Res Inst Nanjing 211125 Peoples R China State Grid Jiangsu Elect Power Co Nanjing 210018 Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Peoples R China

table structure recognition (TSR) is pivotal in document analysis, especially in end-to-end automated identification, which profoundly impacts decision-making and efficiency spanning diverse fields such as power industry, education, healthcare, and scientific papers. However, accurately identifying table structures remains challenging due to complex layouts and irregular distortions. Considering the complex structure of tables in power relay protection systems, this study proposes a novel end-to-end TSR model, leveraging deep learning and innovative modules to address these challenges. Specifically, the new model TSR-Net replaces YOLOv8's backbone with our proposed DSConv-Variants, improving accuracy and reducing parameters. Additionally, a Multi-Scale Attention Module (MAM) captures semantic features efficiently, while a Dynamic Calibration Module (DCM) enhances cross-layer fusion. Experimental results demonstrate the superior performance of our approach on public benchmarks, showcasing its utility for TSR.

关键词： Feature extraction Accuracy Solid modeling Adaptation models Shape Distortion Semantics Information retrieval Convolutional neural networks Information extraction document understanding table detection table structure recognition table interpretation deep learning deformable convolutional neural networks

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Guided table structure recognition Through Anchor Optimization

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IEEE ACCESS 2021年 9卷 113521-113534页

作者： Hashmi, Khurram Azeem Stricker, Didier Liwicki, Marcus Afzal, Muhammad Noman Afzal, Muhammad Zeshan German Res Ctr Artificial Intelligence D-67663 Kaiserslautern Germany Univ Kaiserslautern Dept Comp Sci D-67663 Kaiserslautern Germany Univ Kaiserslautern Mindgrage D-67663 Kaiserslautern Germany Lulea Univ Technol Dept Comp Sci S-97187 Lulea Sweden Bilojix Soft Technol Bahawalpur 63100 Pakistan

This paper presents the novel approach towards table structure recognition by leveraging the guided anchors. The concept differs from current state-of-the-art systems for table structure recognition that naively apply object detection methods. In contrast to prior techniques, first, we estimate the viable anchors for table structure recognition. Subsequently, these anchors are exploited to locate the rows and columns in tabular images. Furthermore, the paper introduces a simple and effective method that improves the results using tabular layouts in realistic scenarios. The proposed method is exhaustively evaluated on the two publicly available datasets of table structure recognition: ICDAR-2013 and TabStructDB. Moreover, we empirically established the validity of our method by implementing it on the previous approaches. We accomplished state-of-the-art results on the ICDAR-2013 dataset with an average F1-measure of 94.19% (92.06% for rows and 96.32% for columns). Thus, a relative error reduction of more than 25% is achieved. Furthermore, our proposed post-processing improves the average F1-measure to 95.46% that results in a relative error reduction of more than 35%. Moreover, we surpassed the baseline results on the TabStructDB dataset with an average F1-measure of 94.57% (94.08% for rows and 95.06% for columns).

关键词： Task analysis Semantics Portable document format Object detection Image recognition Annotations Optimization Deep neural network Mask R-CNN document images object detection anchor optimization guided anchors table structure recognition table structure extraction table understanding

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tableStrRec: framework for table structure recognition in data sheet images

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INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND recognition 2024年第2期27卷 127-145页

作者： Fernandes, Johan Xiao, Bin Simsek, Murat Kantarci, Burak Khan, Shahzad Alkheir, Ala Abu Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON K1N 6N5 Canada Gnowit 383 Celt Ridge Cres Ottawa ON K2W 0B5 Canada Lytica 555 Legget Dr Ottawa ON K2K 2X3 Canada

Billions of documents in data sheet format are shared between various organizations across the globe on a daily basis. The essential information in these documents is presented in tabular format. Extracting and assimilating this information can help organizations make data-driven decisions. Solutions for detecting tables in document images have been well explored. Thus, in this work, we propose tableStrRec, a deep learning-based approach to recognize the structure of such detected tables by detecting rows and columns. tableStrRec comprises two Cascade R-CNN architectures, each with a deformable backbone and Complete IOU loss to improve their detection performance. One architecture detects and classifies rows as regular rows (rows without a merged cell) and irregular rows (groups of regular rows that share a merged cell). The second architecture detects and classifies columns as regular columns (columns without a merged cell) and irregular columns (groups of regular columns that share a merged cell). Both architectures work in parallel to provide the results in a single inference. We show that utilizing tableStrRec to detect four classes of objects improves the table structure recognition performance on three public test sets. We achieve 90.5% and 89.6% weighted average F1 scores on the ICDAR2013 test set for rows and columns, respectively. On the TabStructDB test set, we achieve 72.7% and 78.5% weighted average F1 score for rows and columns, respectively. We also evaluate the proposed method under the FinTabNet dataset using the structure-only TEDS score, achieving 98.34%, which can outperform most state-of-the-art benchmark models.

关键词： Deep learning Deformable convolutional neural networks Image processing Document analysis table structure recognition Row and column detection Page object detection

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