Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection

• URL: http://arxiv.org/abs/2003.07493v2
• Date: Sun, 30 Aug 2020 07:36:56 GMT
• Title: Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection
• Authors: Shi-Xue Zhang, Xiaobin Zhu, Jie-Bo Hou, Chang Liu, Chun Yang, Hongfa Wang, Xu-Cheng Yin
• Abstract summary: We propose a novel unified relational reasoning graph network for arbitrary shape text detection. An innovative local graph bridges a text proposal model via CNN and a deep relational reasoning network via Graph Convolutional Network (GCN) Experiments on public available datasets demonstrate the state-of-the-art performance of our method.
• Score: 20.244378408779554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Arbitrary shape text detection is a challenging task due to the high variety and complexity of scenes texts. In this paper, we propose a novel unified relational reasoning graph network for arbitrary shape text detection. In our method, an innovative local graph bridges a text proposal model via Convolutional Neural Network (CNN) and a deep relational reasoning network via Graph Convolutional Network (GCN), making our network end-to-end trainable. To be concrete, every text instance will be divided into a series of small rectangular components, and the geometry attributes (e.g., height, width, and orientation) of the small components will be estimated by our text proposal model. Given the geometry attributes, the local graph construction model can roughly establish linkages between different text components. For further reasoning and deducing the likelihood of linkages between the component and its neighbors, we adopt a graph-based network to perform deep relational reasoning on local graphs. Experiments on public available datasets demonstrate the state-of-the-art performance of our method.

Related papers

• Bridging Local Details and Global Context in Text-Attributed Graphs [62.522550655068336]
  GraphBridge is a framework that bridges local and global perspectives by leveraging contextual textual information. Our method achieves state-of-theart performance, while our graph-aware token reduction module significantly enhances efficiency and solves scalability issues.
  arXiv  Detail & Related papers  (2024-06-18T13:35:25Z)
• GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram Intersection [51.608147732998994]
  Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning. We propose a new graph neural network architecture that substitutes classical message passing with an analysis of the local distribution of node features.
  arXiv  Detail & Related papers  (2024-01-17T13:04:23Z)
• Pretraining Language Models with Text-Attributed Heterogeneous Graphs [28.579509154284448]
  We present a new pretraining framework for Language Models (LMs) that explicitly considers the topological and heterogeneous information in Text-Attributed Heterogeneous Graphs (TAHGs) We propose a topology-aware pretraining task to predict nodes involved in the context graph by jointly optimizing an LM and an auxiliary heterogeneous graph neural network. We conduct link prediction and node classification tasks on three datasets from various domains.
  arXiv  Detail & Related papers  (2023-10-19T08:41:21Z)
• SynGraphy: Succinct Summarisation of Large Networks via Small Synthetic Representative Graphs [4.550112751061436]
  We describe SynGraphy, a method for visually summarising the structure of large network datasets. It works by drawing smaller graphs generated to have similar structural properties to the input graphs.
  arXiv  Detail & Related papers  (2023-02-15T16:00:15Z)
• Improving Graph-Based Text Representations with Character and Word Level N-grams [30.699644290131044]
  We propose a new word-character text graph that combines word and character n-gram nodes together with document nodes. We also propose two new graph-based neural models, WCTextGCN and WCTextGAT, for modeling our proposed text graph.
  arXiv  Detail & Related papers  (2022-10-12T08:07:54Z)
• TeKo: Text-Rich Graph Neural Networks with External Knowledge [75.91477450060808]
  We propose a novel text-rich graph neural network with external knowledge (TeKo) We first present a flexible heterogeneous semantic network that incorporates high-quality entities. We then introduce two types of external knowledge, that is, structured triplets and unstructured entity description.
  arXiv  Detail & Related papers  (2022-06-15T02:33:10Z)
• StrokeNet: Stroke Assisted and Hierarchical Graph Reasoning Networks [31.76016966100244]
  StrokeNet is proposed to effectively detect the texts by capturing the fine-grained strokes. Different from existing approaches that represent the text area by a series of points or rectangular boxes, we directly localize strokes of each text instance.
  arXiv  Detail & Related papers  (2021-11-23T08:26:42Z)
• GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph [53.70520466556453]
  We propose GraphFormers, where layerwise GNN components are nested alongside the transformer blocks of language models. With the proposed architecture, the text encoding and the graph aggregation are fused into an iterative workflow. In addition, a progressive learning strategy is introduced, where the model is successively trained on manipulated data and original data to reinforce its capability of integrating information on graph.
  arXiv  Detail & Related papers  (2021-05-06T12:20:41Z)
• Towards Efficient Scene Understanding via Squeeze Reasoning [71.1139549949694]
  We propose a novel framework called Squeeze Reasoning. Instead of propagating information on the spatial map, we first learn to squeeze the input feature into a channel-wise global vector. We show that our approach can be modularized as an end-to-end trained block and can be easily plugged into existing networks.
  arXiv  Detail & Related papers  (2020-11-06T12:17:01Z)
• Spectral Embedding of Graph Networks [76.27138343125985]
  We introduce an unsupervised graph embedding that trades off local node similarity and connectivity, and global structure. The embedding is based on a generalized graph Laplacian, whose eigenvectors compactly capture both network structure and neighborhood proximity in a single representation.
  arXiv  Detail & Related papers  (2020-09-30T04:59:10Z)
• PuzzleNet: Scene Text Detection by Segment Context Graph Learning [9.701699882807251]
  We propose a novel decomposition-based method, termed Puzzle Networks (PuzzleNet), to address the challenging scene text detection task. By building segments as context graphs, MSGCN effectively employs segment context to predict combinations of segments. Our method can achieve better or comparable performance than current state-of-the-arts, which is beneficial from the exploitation of segment context graph.
  arXiv  Detail & Related papers  (2020-02-26T09:21:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.