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
- pp 1484-1493
TLDR
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.Abstract:
This report presents the final results of the ICDAR 2013 Robust Reading Competition. The competition is structured in three Challenges addressing text extraction in different application domains, namely born-digital images, real scene images and real-scene videos. The Challenges are organised around specific tasks covering text localisation, text segmentation and word recognition. The competition took place in the first quarter of 2013, and received a total of 42 submissions over the different tasks offered. This report describes the datasets and ground truth specification, details the performance evaluation protocols used and presents the final results along with a brief summary of the participating methods.read more
Citations
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Journal ArticleDOI
An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition
Baoguang Shi,Xiang Bai,Cong Yao +2 more
TL;DR: Zhang et al. as mentioned in this paper proposed a novel neural network architecture, which integrates feature extraction, sequence modeling and transcription into a unified framework, and achieved remarkable performances in both lexicon free and lexicon-based scene text recognition tasks.
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
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
Speeding up Convolutional Neural Networks with Low Rank Expansions
TL;DR: Two simple schemes for drastically speeding up convolutional neural networks are presented, achieved by exploiting cross-channel or filter redundancy to construct a low rank basis of filters that are rank-1 in the spatial domain.
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.
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