Table Recognition
28 papers with code • 5 benchmarks • 7 datasets
Table recognition refers to the process of automatically identifying and extracting tabular structures from unstructured data sources such as text documents, images, or scanned documents. The goal of table recognition is to accurately detect the presence of tables within the data and extract their contents, including rows, columns, headers, and cell values.
Benchmarks
These leaderboards are used to track progress in Table Recognition
Libraries
Use these libraries to find Table Recognition models and implementations
PubTabNet
FinTabNet
WTW
PubTables-1M
TNCR Dataset
CISOL
Most implemented papers
Image-based table recognition: data, model, and evaluation
In addition, we propose a new Tree-Edit-Distance-based Similarity (TEDS) metric for table recognition, which more appropriately captures multi-hop cell misalignment and OCR errors than the pre-established metric.
Rethinking Table Recognition using Graph Neural Networks
In this paper, we propose an architecture based on graph networks as a better alternative to standard neural networks for table recognition.
CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents
DevashishPrasad/CascadeTabNet
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In this paper, we present an improved deep learning-based end to end approach for solving both problems of table detection and structure recognition using a single Convolution Neural Network (CNN) model.
PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML
In our method, we divide the table content recognition task into foursub-tasks: table structure recognition, text line detection, text line recognition, and box assignment. Our table structure recognition algorithm is customized based on MASTER [1], a robust image textrecognition algorithm.
ICDAR 2021 Competition on Scientific Literature Parsing
Scientific literature contain important information related to cutting-edge innovations in diverse domains.
Deep learning for table detection and structure recognition: A survey
The goals of this survey are to provide a profound comprehension of the major developments in the field of Table Detection, offer insight into the different methodologies, and provide a systematic taxonomy of the different approaches.
LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment
hikopensource/davar-lab-ocr
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In this paper, we aim to obtain more reliable aligned bounding boxes by fully utilizing the visual information from both text regions in proposed local features and cell relations in global features.
PubTables-1M: Towards comprehensive table extraction from unstructured documents
microsoft/table-transformer
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CVPR 2022
We demonstrate that these improvements lead to a significant increase in training performance and a more reliable estimate of model performance at evaluation for table structure recognition.
LORE: Logical Location Regression Network for Table Structure Recognition
alibabaresearch/advancedliteratemachinery
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Table structure recognition (TSR) aims at extracting tables in images into machine-understandable formats.
An End-to-End Multi-Task Learning Model for Image-based Table Recognition
Most of the previous methods focus on a non-end-to-end approach which divides the problem into two separate sub-problems: table structure recognition; and cell-content recognition and then attempts to solve each sub-problem independently using two separate systems.
