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Found 5 papers, 0 papers with code
Dynamic Relation Transformer for Contextual Text Block Detection
Contextual Text Block Detection (CTBD) is the task of identifying coherent text blocks within the complexity of natural scenes.
Robust Table Structure Recognition with Dynamic Queries Enhanced Detection Transformer
Unlike previous methods, we formulate table separation line prediction as a line regression problem instead of an image segmentation problem and propose a new two-stage dynamic queries enhanced DETR based separation line regression approach, named DQ-DETR, to predict separation lines from table images directly.
TSRFormer: Table Structure Recognition with Transformers
We present a new table structure recognition (TSR) approach, called TSRFormer, to robustly recognizing the structures of complex tables with geometrical distortions from various table images.
Ranked #2 on
Table Recognition
on PubTabNet
(TEDS-Struct metric)
Robust Table Detection and Structure Recognition from Heterogeneous Document Images
We introduce a new table detection and structure recognition approach named RobusTabNet to detect the boundaries of tables and reconstruct the cellular structure of each table from heterogeneous document images.
Ranked #5 on
Table Recognition
on PubTabNet
(TEDS-Struct metric)
ReLaText: Exploiting Visual Relationships for Arbitrary-Shaped Scene Text Detection with Graph Convolutional Networks
The key idea is to decompose text detection into two subproblems, namely detection of text primitives and prediction of link relationships between nearby text primitive pairs.
