Proceedings ArticleDOI
ICDAR 2015 competition on Robust Reading
Dimosthenis Karatzas,Lluis Gomez-Bigorda,Anguelos Nicolaou,Suman K. Ghosh,Andrew D. Bagdanov,Masakazu Iwamura,Jiri Matas,Lukas Neumann,Vijay Chandrasekhar,Shijian Lu,Faisal Shafait,Seiichi Uchida,Ernest Valveny +12 more
- pp 1156-1160
TLDR
A new Challenge 4 on Incidental Scene Text has been added to the Challenges on Born-Digital Images, Focused Scene Images and Video Text and tasks assessing End-to-End system performance have been introduced to all Challenges.Abstract:
Results of the ICDAR 2015 Robust Reading Competition are presented. A new Challenge 4 on Incidental Scene Text has been added to the Challenges on Born-Digital Images, Focused Scene Images and Video Text. Challenge 4 is run on a newly acquired dataset of 1,670 images evaluating Text Localisation, Word Recognition and End-to-End pipelines. In addition, the dataset for Challenge 3 on Video Text has been substantially updated with more video sequences and more accurate ground truth data. Finally, tasks assessing End-to-End system performance have been introduced to all Challenges. The competition took place in the first quarter of 2015, and received a total of 44 submissions. Only the tasks newly introduced in 2015 are reported on. The datasets, the ground truth specification and the evaluation protocols are presented together with the results and a brief summary of the participating methods.read more
Citations
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Proceedings ArticleDOI
DOTA: A Large-Scale Dataset for Object Detection in Aerial Images
Gui-Song Xia,Xiang Bai,Jian Ding,Zhen Zhu,Serge Belongie,Jiebo Luo,Mihai Datcu,Marcello Pelillo,Liangpei Zhang +8 more
TL;DR: The Dataset for Object Detection in Aerial Images (DOTA) as discussed by the authors is a large-scale dataset of aerial images collected from different sensors and platforms and contains objects exhibiting a wide variety of scales, orientations, and shapes.
Proceedings ArticleDOI
EAST: An Efficient and Accurate Scene Text Detector
TL;DR: This work proposes a simple yet powerful pipeline that yields fast and accurate text detection in natural scenes, and significantly outperforms state-of-the-art methods in terms of both accuracy and efficiency.
Proceedings ArticleDOI
Synthetic Data for Text Localisation in Natural Images
TL;DR: In this article, a Fully-Convolutional Regression Network (FCRN) was proposed to perform text detection and bounding-box regression at all locations and multiple scales in an image.
Journal ArticleDOI
Arbitrary-Oriented Scene Text Detection via Rotation Proposals
TL;DR: The Rotation Region Proposal Networks are designed to generate inclined proposals with text orientation angle information that are adapted for bounding box regression to make the proposals more accurately fit into the text region in terms of the orientation.
Book ChapterDOI
Detecting Text in Natural Image with Connectionist Text Proposal Network
TL;DR: The Connectionist Text Proposal Network (CTPN) as mentioned in this paper detects a text line in a sequence of fine-scale text proposals directly in convolutional feature maps, and develops a vertical anchor mechanism that jointly predicts location and text/non-text score of each fixed-width proposal.
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