Proceedings ArticleDOI
ICDAR2017 Robust Reading Challenge on Multi-Lingual Scene Text Detection and Script Identification - RRC-MLT
Nibal Nayef,Fei Yin,Imen Bizid,Hyun-Soo Choi,Yuan Feng,Dimosthenis Karatzas,Zhenbo Luo,Umapada Pal,Christophe Rigaud,Joseph Chazalon,Wafa Khlif,Muhammad Muzzamil Luqman,Jean-Christophe Burie,Cheng-Lin Liu,Jean-Marc Ogier +14 more
- pp 1454-1459
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TLDR
This paper presents the dataset, the tasks and the findings of this RRC-MLT challenge, which aims at assessing the ability of state-of-the-art methods to detect Multi-Lingual Text in scene images, such as in contents gathered from the Internet media and in modern cities where multiple cultures live and communicate together.Abstract:
Text detection and recognition in a natural environment are key components of many applications, ranging from business card digitization to shop indexation in a street. This competition aims at assessing the ability of state-of-the-art methods to detect Multi-Lingual Text (MLT) in scene images, such as in contents gathered from the Internet media and in modern cities where multiple cultures live and communicate together. This competition is an extension of the Robust Reading Competition (RRC) which has been held since 2003 both in ICDAR and in an online context. The proposed competition is presented as a new challenge of the RRC. The dataset built for this challenge largely extends the previous RRC editions in many aspects: the multi-lingual text, the size of the dataset, the multi-oriented text, the wide variety of scenes. The dataset is comprised of 18,000 images which contain text belonging to 9 languages. The challenge is comprised of three tasks related to text detection and script classification. We have received a total of 16 participations from the research and industrial communities. This paper presents the dataset, the tasks and the findings of this RRC-MLT challenge.read more
Citations
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Proceedings ArticleDOI
Character Region Awareness for Text Detection
TL;DR: Zhang et al. as mentioned in this paper proposed a new scene text detection method to effectively detect text area by exploring each character and affinity between characters, which significantly outperforms the state-of-the-art detectors.
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Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes
TL;DR: This paper investigates the problem of scene text spotting, which aims at simultaneous text detection and recognition in natural images, and proposes an end-to-end trainable neural network model, named as Mask TextSpotter, which is inspired by the newly published work Mask R-CNN.
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R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object
TL;DR: The key idea of feature refinement module is to re-encode the position information of the current refined bounding box to the corresponding feature points through feature interpolation to realize feature reconstruction and alignment.
Book ChapterDOI
Arbitrary-Oriented Object Detection with Circular Smooth Label
Xue Yang,Junchi Yan +1 more
TL;DR: This paper designs a new rotation detection baseline, to address the boundary problem by transforming angular prediction from a regression problem to a classification task with little accuracy loss, whereby high-precision angle classification is devised in contrast to previous works using coarse-granularity in rotation detection.
Journal ArticleDOI
Scene Text Detection and Recognition: The Deep Learning Era
Shangbang Long,Xin He,Cong Yao +2 more
TL;DR: Jiang et al. as mentioned in this paper summarized and analyzed the major changes and significant progresses of scene text detection and recognition in the deep learning era, highlighting recent techniques and benchmarks, and looking ahead into future trends.
References
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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
TL;DR: 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.
Proceedings ArticleDOI
ICDAR 2013 Robust Reading Competition
Dimosthenis Karatzas,Faisal Shafait,Seiichi Uchida,Masakazu Iwamura,Lluís Gómez i Bigorda,Sergi Robles Mestre,Joan Mas,David Fernandez Mota,Jon Almazan,Lluís-Pere de las Heras +9 more
TL;DR: The datasets and ground truth specification are described, the performance evaluation protocols used are details, and the final results are presented along with a brief summary of the participating methods.
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.
Journal ArticleDOI
Arbitrary-Oriented Scene Text Detection via Rotation Proposals
TL;DR: RRPN as mentioned in this paper proposes a rotation region proposal network to generate inclined text proposals with text orientation angle information, which is then adapted for bounding box regression to make the proposals more accurately fit into the text region in terms of the orientation.
Proceedings ArticleDOI
Deep Direct Regression for Multi-oriented Scene Text Detection
TL;DR: A deep direct regression based method for multi-oriented scene text detection that achieves the F-measure of 81%, which is a new state-of-the-art and significantly outperforms previous approaches.