23 citations · 50 across the 9 of their papers we have counts for
9 papers
Hierarchical Text Spotter for Joint Text Spotting and Layout Analysis
Shangbang Long, Siyang Qin, Yasuhisa Fujii +2
We propose Hierarchical Text Spotter (HTS), a novel method for the joint task of word-level text spotting and geometric layout analysis. HTS can recognize text in an image and iden…
FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction
Chen-Yu Lee, Chun-Liang Li, Hao Zhang +13
The recent advent of self-supervised pre-training techniques has led to a surge in the use of multimodal learning in form document understanding. However, existing approaches that…
ICDAR 2023 Competition on Hierarchical Text Detection and Recognition
Shangbang Long, Siyang Qin, Dmitry Panteleev +3
We organize a competition on hierarchical text detection and recognition. The competition is aimed to promote research into deep learning models and systems that can jointly perfor…
Towards End-to-End Unified Scene Text Detection and Layout Analysis
Shangbang Long, Siyang Qin, Dmitry Panteleev +3
Scene text detection and document layout analysis have long been treated as two separate tasks in different image domains. In this paper, we bring them together and introduce the t…
ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction
Chen-Yu Lee, Chun-Liang Li, Chu Wang +5
Natural reading orders of words are crucial for information extraction from form-like documents. Despite recent advances in Graph Convolutional Networks (GCNs) on modeling spatial…
Rethinking Text Line Recognition Models
Daniel Hernandez Diaz, Siyang Qin, Reeve Ingle +2
In this paper, we study the problem of text line recognition. Unlike most approaches targeting specific domains such as scene-text or handwritten documents, we investigate the gene…
Towards Unconstrained End-to-End Text Spotting
Siyang Qin, Alessandro Bissacco, Michalis Raptis +2
We propose an end-to-end trainable network that can simultaneously detect and recognize text of arbitrary shape, making substantial progress on the open problem of reading scene te…
Automatic Semantic Content Removal by Learning to Neglect
Siyang Qin, Jiahui Wei, Roberto Manduchi
We introduce a new system for automatic image content removal and inpainting. Unlike traditional inpainting algorithms, which require advance knowledge of the region to be filled i…
Cascaded Segmentation-Detection Networks for Word-Level Text Spotting
Siyang Qin, Roberto Manduchi
We introduce an algorithm for word-level text spotting that is able to accurately and reliably determine the bounding regions of individual words of text "in the wild". Our system…