Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context
论文引用
Automated scholarly paper review: Technologies and challenges
Table understanding: Problem overview
A. Shigarov,
2022,
WIREs Data Mining Knowl. Discov.
Semantic Table Detection with LayoutLMv3
TSRFormer: Table Structure Recognition with Transformers
Qiang Huo, Lei Sun, Weihong Lin et al.,
2022,
ACM Multimedia
PubTables-1M: Towards a universal dataset and metrics for training and evaluating table extraction models
Neural Collaborative Graph Machines for Table Structure Recognition
Hao Liu, Xin Li, Deqiang Jiang et al.,
2021,
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Deep-learning and graph-based approach to table structure recognition
N. Cho, Eunji Lee, Jaewoo Park et al.,
2021,
Multimedia tools and applications
Automated scholarly paper review: Concepts, technologies, and challenges
Yidong Chen, X. Shi, Jialiang Lin et al.,
2021,
Inf. Fusion
LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment
Fei Wu, Wenqi Ren, Shiliang Pu et al.,
2021,
ICDAR
Synthetic Document Generator for Annotation-free Layout Recognition
Sameena Shah, Natraj Raman, M. Veloso,
2021,
Pattern Recognit.
DUE: End-to-End Document Understanding Benchmark
M. Turski, Michal Pietruszka, Karolina Szyndler et al.,
2021,
NeurIPS Datasets and Benchmarks
TABBIE: Pretrained Representations of Tabular Data
H. Iida, Mohit Iyyer, Dung Ngoc Thai et al.,
2021,
NAACL
Scientific evidence extraction