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Found 9 papers, 4 papers with code
Hierarchical Text Spotter for Joint Text Spotting and Layout Analysis
We propose Hierarchical Text Spotter (HTS), a novel method for the joint task of word-level text spotting and geometric layout analysis.
ICDAR 2023 Competition on Hierarchical Text Detection and Recognition
We organize a competition on hierarchical text detection and recognition.
FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction
In FormNetV2, we introduce a centralized multimodal graph contrastive learning strategy to unify self-supervised pre-training for all modalities in one loss.
Towards End-to-End Unified Scene Text Detection and Layout Analysis
In this paper, we bring them together and introduce the task of unified scene text detection and layout analysis.
ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction
Natural reading orders of words are crucial for information extraction from form-like documents.
Rethinking Text Line Recognition Models
Unlike the more common Transformer-based models, this architecture can handle inputs of arbitrary length, a requirement for universal line recognition.
Ranked #2 on
Handwritten Text Recognition
on IAM
(using extra training data)
Towards Unconstrained End-to-End Text Spotting
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 text of irregular shape.
Instance Segmentation
Optical Character Recognition (OCR)
+3
Automatic Semantic Content Removal by Learning to Neglect
We introduce a new system for automatic image content removal and inpainting.
Cascaded Segmentation-Detection Networks for Word-Level Text Spotting
The second network (inspired by the popular YOLO architecture) analyzes each segment produced in the first stage, and predicts oriented rectangular regions containing individual words.
