Detecting Text in Natural Image with Connectionist Text Proposal Network

@inproceedings{Tian2016DetectingTI,
  title={Detecting Text in Natural Image with Connectionist Text Proposal Network},
  author={Zhi Tian and Weilin Huang and Tong He and Pan He and Yu Qiao},
  booktitle={ECCV},
  year={2016}
}
We propose a novel Connectionist Text Proposal Network (CTPN) that accurately localizes text lines in natural image. [...] Key Method The sequential proposals are naturally connected by a recurrent neural network, which is seamlessly incorporated into the convolutional network, resulting in an end-to-end trainable model. This allows the CTPN to explore rich context information of image, making it powerful to detect extremely ambiguous text.Expand
Natural Scene Text Detection Based on Deep Supervised Fully Convolutional Network
TLDR
Experimental results on the ICDAR 2015 datasets and MSRA-TD500 datasets have proven that the proposed method outperforms the state-of-the-art methods by a noticeable margin on F-score.
Orientation-Aware Text Proposals Network for Scene Text Detection
TLDR
The OA-TPN is able to accurately localize arbitrary-oriented text lines in a natural image and works reliably on multi-scale and multi-orientation text with single-scale images.
Detecting Text in News Images with Similarity Embedded Proposals
TLDR
An effective news text detection framework is developed by introducing a novel similarity embedded proposal mechanism to predict similarity for each fine-scale coarse proposal to help construct text bounding boxes.
Detecting Oriented Text in Natural Images by Linking Segments
TLDR
SegLink, an oriented text detection method to decompose text into two locally detectable elements, namely segments and links, achieves an f-measure of 75.0% on the standard ICDAR 2015 Incidental (Challenge 4) benchmark, outperforming the previous best by a large margin.
Detecting Text in the Wild with Deep Character Embedding Network
TLDR
A deep character embedding network (CENet) is proposed which simultaneously predicts the bounding boxes of characters and their embedding vectors, thus making text detection a simple clustering task in thecharacter embedding space.
Single Shot Text Detector with Regional Attention
TLDR
A novel single-shot text detector that directly outputs word-level bounding boxes in a natural image and develops a hierarchical inception module which efficiently aggregates multi-scale inception features.
Towards Accurate Scene Text Detection with Bidirectional Feature Pyramid Network
TLDR
A new Fully Convolutional One-Stage Object Detection (FCOS)-based text detection method that can robustly detect multioriented and multilingual text from natural scene images in a per pixel prediction approach and applies the Bidirectional Feature Pyramid Network (BiFPN) as the backbone network, enhancing the model learning capacity and increasing the receptive field.
Text Detection in Natural Scene Images with Text Line Construction
  • Zihao Liu, Qiwei Shen, Chun Wang
  • Computer Science
    2018 IEEE International Conference on Information Communication and Signal Processing (ICICSP)
  • 2018
TLDR
A new text region recognition algorithm that can accurately localize image text regions in natural image with complex background based on the anchor mechanism of the faster R-CNN, taking into account the special features of the text area relative to other object detect tasks.
TextNet: Irregular Text Reading from Images with an End-to-End Trainable Network
TLDR
An end-to-end trainable network architecture, named TextNet, is proposed, which is able to simultaneously localize and recognize irregular text from images, and can achieve state-of-the-art performance on irregular datasets by a large margin.
Convolutional Regression Network for Multi-Oriented Text Detection
TLDR
A novel convolutional regression network (CRN) to localize multi-oriented text in natural images, which consists of two components: region proposal extractor and text locator, and can be trained in an end-to-end mechanism which is suitable for detecting multi- oriented texts.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 39 REFERENCES
Accurate Text Localization in Natural Image with Cascaded Convolutional Text Network
TLDR
A novel Cascaded Convolutional Text Network (CCTN) is proposed that joints two customized convolutional networks for coarse-to-fine text localization and exhibits surprising robustness and discriminative power.
Text-Attentional Convolutional Neural Network for Scene Text Detection
TLDR
A new system for scene text detection by proposing a novel text-attentional convolutional neural network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components and a powerful low-level detector called contrast-enhancement maximally stable extremal regions (MSERs) is developed.
Reading Scene Text in Deep Convolutional Sequences
TLDR
A deep recurrent model, building on long short-term memory (LSTM), is developed to robustly recognize the generated CNN sequences, departing from most existing approaches recognising each character independently.
Reading Text in the Wild with Convolutional Neural Networks
TLDR
An end-to-end system for text spotting—localising and recognising text in natural scene images—and text based image retrieval and a real-world application to allow thousands of hours of news footage to be instantly searchable via a text query is demonstrated.
Multi-oriented Text Detection with Fully Convolutional Networks
TLDR
A novel approach for text detection in natural images that consistently achieves the state-of-the-art performance on three text detection benchmarks: MSRA-TD500, I CDAR2015 and ICDAR2013.
Text Flow: A Unified Text Detection System in Natural Scene Images
TLDR
The proposed unified scene text detection system, namely Text Flow, is proposed by utilizing the minimum cost (min-cost) flow network model and it outperforms the state-of-the-art methods on all three datasets with much higher recall and F-score.
A Hybrid Approach to Detect and Localize Texts in Natural Scene Images
TLDR
A hybrid approach to robustly detect and localize texts in natural scene images using a text region detector, a conditional random field model, and a learning-based energy minimization method are presented.
Robust Scene Text Detection with Convolution Neural Network Induced MSER Trees
TLDR
A novel framework to tackle the problem of distinguishing texts from background components by leveraging the high capability of convolutional neural network (CNN), capable of learning high-level features to robustly identify text components from text-like outliers.
Deep Features for Text Spotting
TLDR
A Convolutional Neural Network classifier is developed that can be used for text spotting in natural images and a method of automated data mining of Flickr, that generates word and character level annotations is used to form an end-to-end, state-of-the-art text spotting system.
Text Localization in Natural Images Using Stroke Feature Transform and Text Covariance Descriptors
TLDR
A powerful low-level filter called the Stroke Feature Transform (SFT) is proposed, which extends the widely-used Stroke Width Transform (SWT) by incorporating color cues of text pixels, leading to significantly enhanced performance on inter-component separation and intra-component connection.
...
1
2
3
4
...