Journal ArticleDOI
Scene Text Detection via Connected Component Clustering and Nontext Filtering
Hyung Il Koo,Duck Hoon Kim +1 more
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TLDR
A new scene text detection algorithm based on two machine learning classifiers that allows us to generate candidate word regions and the other filters out nontext ones, and extends the approach to exploit multichannel information.Abstract:
In this paper, we present a new scene text detection algorithm based on two machine learning classifiers: one allows us to generate candidate word regions and the other filters out nontext ones. To be precise, we extract connected components (CCs) in images by using the maximally stable extremal region algorithm. These extracted CCs are partitioned into clusters so that we can generate candidate regions. Unlike conventional methods relying on heuristic rules in clustering, we train an AdaBoost classifier that determines the adjacency relationship and cluster CCs by using their pairwise relations. Then we normalize candidate word regions and determine whether each region contains text or not. Since the scale, skew, and color of each candidate can be estimated from CCs, we develop a text/nontext classifier for normalized images. This classifier is based on multilayer perceptrons and we can control recall and precision rates with a single free parameter. Finally, we extend our approach to exploit multichannel information. Experimental results on ICDAR 2005 and 2011 robust reading competition datasets show that our method yields the state-of-the-art performance both in speed and accuracy.read more
Citations
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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.
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.
Journal ArticleDOI
Text Detection and Recognition in Imagery: A Survey
Qixiang Ye,David Doermann +1 more
TL;DR: This review provides a fundamental comparison and analysis of the remaining problems in the field and summarizes the fundamental problems and enumerates factors that should be considered when addressing these problems.
Journal ArticleDOI
Robust Text Detection in Natural Scene Images
TL;DR: An accurate and robust method for detecting texts in natural scene images using a fast and effective pruning algorithm to extract Maximally Stable Extremal Regions (MSERs) as character candidates using the strategy of minimizing regularized variations is proposed.
References
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Robust wide-baseline stereo from maximally stable extremal regions
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Proceedings ArticleDOI
Robust wide baseline stereo from maximally stable extremal regions
TL;DR: The wide-baseline stereo problem, i.e. the problem of establishing correspondences between a pair of images taken from different viewpoints, is studied and an efficient and practically fast detection algorithm is presented for an affinely-invariant stable subset of extremal regions, the maximally stable extremal region (MSER).