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tableGPT: a novel table understanding method based on table recognition and large language model collaborative enhancement

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APPLIED INTELLIGENCE 2025年第4期55卷 1-25页

作者： Ren, Yi Yu, Chenglong Li, Weibin Li, Wei Zhu, Zixuan Zhang, Tianyi Qin, Chenhao Ji, Wenbo Zhang, Jianjun Xidian Univ Lab AI Hangzhou Inst Technol Hangzhou 311231 Peoples R China Xidian Univ Sch Artif Intelligence Xian 710071 Peoples R China Xi Xian Rail Transit Investment & Construct Co Ltd 2Taibai South Rd Xian 710086 Shaanxi Peoples R China

In today's information age, table images play a crucial role in storing structured information, making table image recognition technology an essential component in many fields. However, accurately recognizing the structure and text content of various complex table images has remained a challenge. Recently, large language models (LLMs) have demonstrated exceptional capabilities in various natural language processing tasks. Therefore, applying LLMs to the correction tasks of structure and text content after table image recognition presents a novel solution. This paper introduces a new method, tableGPT, which combines table recognition with LLMs and develops a specialized multimodal agent to enhance the effectiveness of table image recognition. Our approach is divided into four stages. In the first stage, tableGPT_agent initially evaluates whether the input is a table image and, upon confirmation, uses algorithms such as the transformer for preliminary recognition. In the second stage, the agent converts the recognition results into HTML format and autonomously assesses whether corrections are needed. If corrections are needed, the data are input into a trained LLM to achieve more accurate table recognition and optimization. In the third stage, the agent evaluates user satisfaction through feedback and applies superresolution algorithms to low-quality images, as this is often the main reason for user dissatisfaction. Finally, the agent inputs both the enhanced and original images into the trained model, integrating the information to obtain the optimal table text representation. Our research shows that trained LLMs can effectively interpret table images, improving the Tree Edit Distance Similarity (TEDS) score by an average of 4% even when based on the best current table recognition methods, across both public and private datasets. They also demonstrate better performance in correcting structural and textual errors. We also explore the impact of image superresolution t

关键词： table recognition Large language model Image superresolution Agent Prompt words

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table recognition in Spreadsheets via a Graph Representation 13

Table Recognition in Spreadsheets via a Graph Representation

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13th IAPR International Workshop on Document Analysis Systems (DAS)

作者： Koci, Elvis Thiele, Maik Lehner, Wolfgang Romero, Oscar Tech Univ Dresden Dept Comp Sci Dresden Germany Univ Politecn Cataluna BarcelonaTech Dept Engn Serv & Sistemes Informacio Barcelona Spain

ISBN: (纸本)9781538633465

Spreadsheet software are very popular data management tools. Their ease of use and abundant functionalities equip novices and professionals alike with the means to generate, transform, analyze, and visualize data. As a result, spreadsheets are a great resource of factual and structured information. This accentuates the need to automatically understand and extract their contents. In this paper, we present a novel approach for recognizing tables in spreadsheets. Having inferred the layout role of the individual cells, we build layout regions. We encode the spatial interrelations between these regions using a graph representation. Based on this, we propose Remove and Conquer (RAC), an algorithm for table recognition that implements a list of carefully curated rules. An extensive experimental evaluation shows that our approach is viable. We achieve significant accuracy in a dataset of real spreadsheets from various domains.

关键词： Spreadsheet table recognition table Identification Graph Rule-based

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Current Status and Performance Analysis of table recognition in Document Images With Deep Neural Networks

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IEEE ACCESS 2021年 9卷 87663-87685页

作者： Hashmi, Khurram Azeem Liwicki, Marcus Stricker, Didier Afzal, Muhammad Adnan Afzal, Muhammad Ahtsham 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

The first phase of table recognition is to detect the tabular area in a document. Subsequently, the tabular structures are recognized in the second phase in order to extract information from the respective cells. table detection and structural recognition are pivotal problems in the domain of table understanding. However, table analysis is a perplexing task due to the colossal amount of diversity and asymmetry in tables. Therefore, it is an active area of research in document image analysis. Recent advances in the computing capabilities of graphical processing units have enabled the deep neural networks to outperform traditional state-of-the-art machine learning methods. table understanding has substantially benefited from the recent breakthroughs in deep neural networks. However, there has not been a consolidated description of the deep learning methods for table detection and table structure recognition. This review paper provides a thorough analysis of the modern methodologies that utilize deep neural networks. Moreover, it presents a comprehensive understanding of the current state-of-the-art and related challenges of table understanding in document images. The leading datasets and their intricacies have been elaborated along with the quantitative results. Furthermore, a brief overview is given regarding the promising directions that can further improve table analysis in document images.

关键词： Image recognition Deep learning Neural networks Task analysis Feature extraction Portable document format Performance analysis Deep neural network document images deep learning performance evaluation table recognition table detection table structure recognition table analysis

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table recognition from Medical Insurance Claims

Table Recognition from Medical Insurance Claims

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Xiang, Zhenggui People's Insurance Company of China China

ISBN: (纸本)9798350386226

Medical insurance claims usually require the review of various medical receipts. However, it's challenging to recognize table contents from medical receipts because of various form for-mat and printing quality. In this paper, we propose a color-wise context-splitting Transformer-based table recognition method for recognizing table contents from medical insurance claim receipts containing some handwritten invoice amounts under red stamps. © 2024 IEEE.

关键词： Medical Insurance Claims table recognition

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Layout Detection and table recognition - Recent Challenges in Digitizing Historical Documents and Handwritten Tabular Data 24th

Layout Detection and Table Recognition - Recent Challenges i...

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24th International Conference on Theory and Practice of Digital Libraries (TPDL)

作者： Lehenmeier, Constantin Burghardt, Manuel Mischka, Bernadette Univ Lib Regensburg D-93053 Regensburg Germany Univ Leipzig Computat Human D-04109 Leipzig Germany Univ Regensburg D-93053 Regensburg Germany

ISBN: (纸本)9783030549558;9783030549565

In this paper, we discuss the computer-aided processing of handwritten tabular records of historical weather data. The observationes meteorologicae, which are housed by the Regensburg University Library, are one of the oldest collections of weather data in Europe. Starting in 1771, meteorological data was consistently documented in a standardized form over almost 60 years by several writers. The tabular structure, as well as the unconstrained textual layout of comments and the use of historical characters, propose various challenges in layout and text recognition. We present a customized strategy to digitize tabular and handwritten data by combining various state-of-the-art methods for OCR processing to fit the collection. Since the recognition of historical documents still poses major challenges, we provide lessons learned from experimental testing during the first project stages. Our results show that deep learning methods can be used for text recognition and layout detection. However, they are less efficient for the recognition of tabular structures. Furthermore, a tailored approach had to be developed for the historical meteorological characters during the manual creation of ground truth data. The customized system achieved an accuracy rate of 82% for the text recognition of the heterogeneous handwriting and 87% accuracy for layout recognition of the tables.

关键词： Document recognition Handwritten text recognition table recognition Historical documents

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An End-to-End Local Attention Based Model for table recognition 17th

An End-to-End Local Attention Based Model for Table Recognit...

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17th International Conference on Document Analysis and recognition (ICDAR)

作者： Ly, Nam Tuan Takasu, Atsuhiro Natl Inst Informat NII Tokyo Japan

ISBN: (纸本)9783031416781;9783031416798

Recently, due to the rapid development of deep learning, especially Transformer, many Transformer-based methods have been studied and proven to be very powerful for table recognition. However, Transformer-based models usually struggle to process big tables due to the limitation of their global attention mechanism. In this paper, we propose a local attention mechanism to address the limitation of the global attention mechanism. We also present an end-to-end local attention-based model for recognizing both table structure and table cell content from a table image. The proposed model consists of four main components: 1) an encoder for feature extraction;2) the three decoders for the three sub-tasks of the table recognition problem. In the experiments, we evaluate the performance of the proposed model and the effectiveness of the local attention mechanism on the two large-scale datasets: PubTabNet and FinTabNet. The experiment results show that the proposed model outperforms the state-of-the-art methods on all benchmark datasets. Furthermore, we demonstrate the effectiveness of the local attention mechanism for table recognition, especially for big table recognition.

关键词： Local Attention Self-Attention table recognition End-to-End

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A minimal and sufficient way of introducing external knowledge for table recognition in archival documents

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6th International Workshop on Graphic recognition

作者： Martinat, Isaac Couasnon, Bertrand INSA IRISA F-35042 Rennes France INRIA IRISA F-35042 Rennes France

ISBN: (纸本)3540347119

We present a system that recognizes tables in archival documents. Many works were carried out on table recognition but very few on tables of historical documents. These are difficult to analyze because they are often damaged due to their age and conservation. Therefore we have to introduce knowledge to compensate for missing information and noise in these documents. As there is a very important number of documents of a same type, the cost is not significant to introduce this explicit knowledge. We also want to minimalize the cost to adapt the system for a given document type. The precision of the knowledge given by the user is dependent on the quality of the document. The more the document is damaged, the more the specification has to be precise. We will show in this article how an external minimal knowledge can be sufficient for an efficient recognition system for tables in archival documents.

关键词： archival documents knowledge specification structured document analysis table recognition

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A KNOWLEDGE-BASED table recognition METHOD FOR CHINESE BANK STATEMENT IMAGES 23

A KNOWLEDGE-BASED TABLE RECOGNITION METHOD FOR CHINESE BANK ...

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23rd IEEE International Conference on Image Processing (ICIP)

作者： Xu, Liang Fan, Wei Sun, Jun Li, Xin Naoi, Satoshi Fujitsu Res & Dev Ctr Co Ltd Beijing 100025 Peoples R China

ISBN: (纸本)9781467399616

Automatic processing of large volume scanned Chinese bank statements is a urgent demand recently. Conventional methods can not well handle the following challenges of this problem: various layout styles, noises, and especially requirement of fast speed for large Chinese character set. This paper proposes a knowledge based table recognition method to meet fast speed requirement with good accuracy. Two kinds of knowledge are utilized to accelerate the identification of digit columns and the cell recognition: i) geometric knowledge about column alignment and quasi equal digit width, and ii) semantic knowledge about prior format based on the results from an optical character recognition (OCR) engine of digits. Experimental results on a real dataset show the effectiveness of our method.

关键词： table recognition table structure analysis Chinese bank statement

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Comparing Machine Learning Approaches for table recognition in Historical Register Books 13

Comparing Machine Learning Approaches for Table Recognition ...

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13th IAPR International Workshop on Document Analysis Systems (DAS)

作者： Clinchant, Stephane Dejean, Herve Meunier, Jean-Luc Lang, Eva Kleber, Florian Naver Labs Europe Meylan France Arch Bistums Passau Bischofliches Ordinariat Passau Passau Germany TU Wien Comp Vis Lab Vienna Austria

ISBN: (纸本)9781538633465

We present in this paper experiments on table recognition in hand-written register books. We first explain how the problem of row and column detection is modelled, and then compare two Machine Learning approaches (Conditional Random Field and Graph Convolutional Network) for detecting these table elements. Evaluation was conducted on death records provided by the Archives of the Diocese of Passau. With an F-1 score of 89, both methods provide a quality which allows for Information Extraction. Software and dataset are open source/data.

关键词： Machine Learning Document Analysis and Understanding table recognition

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OMNIPARSER: A Unified Framework for Text Spotting, Key Information Extraction and table recognition

OMNIPARSER: A Unified Framework for Text Spotting, Key Infor...

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IEEE/CVF Conference on Computer Vision and Pattern recognition (CVPR)

作者： Wan, Jianqiang Song, Sibo Yu, Wenwen Liu, Yuliang Cheng, Wenqing Huang, Fei Bai, Xiang Yao, Cong Yang, Zhibo Alibaba Grp Hangzhou Peoples R China Huazhong Univ Sci & Technol Hangzhou Peoples R China

ISBN: (纸本)9798350353006

Recently, visually-situated text parsing (VsTP) has experienced notable advancements, driven by the increasing demand for automated document understanding and the emergence of Generative Large Language Models (LLMs) capable of processing document-based questions. Various methods have been proposed to address the challenging problem of VsTP. However, due to the diversified targets and heterogeneous schemas, previous works usually design task-specific architectures and objectives for individual tasks, which inadvertently leads to modal isolation and complex workflow. In this paper, we propose a unified paradigm for parsing visually-situated text across diverse scenarios. Specifically, we devise a universal model, called OmniParser, which can simultaneously handle three typical visually-situated text parsing tasks: text spotting, key information extraction, and table recognition. In OmniParser, all tasks share the unified encoder-decoder architecture, the unified objective: point-conditioned text generation, and the unified input&output representation: prompt & structured sequences. Extensive experiments demonstrate that the proposed OmniParser achieves state-of-the-art (SOTA) or highly competitive performances on 7 datasets for the three visually-situated text parsing tasks, despite its unified, concise design. The code is available at AdvancedLiterateMachinery.

关键词： key information extraction table recognition text spotting unified framework

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