Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classifying Images of Materials: Achieving Viewpoint and Illumination Independence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
ICDAR 2003 Robust Reading Competitions
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Shape Matching and Object Recognition Using Low Distortion Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
WaldBoost " Learning for Time Constrained Sequential Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text Locating Competition Results
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Object count/area graphs for the evaluation of object detection and segmentation algorithms
International Journal on Document Analysis and Recognition
Colour text segmentation in web images based on human perception
Image and Vision Computing
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Text Detection and Localization in Complex Scene Images using Constrained AdaBoost Algorithm
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Text Detection in Urban Scenes
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
Detecting and reading text in natural scenes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Text Detection Using Edge Gradient and Graph Spectrum
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Scene Text Extraction with Edge Constraint and Text Collinearity
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A method for text localization and recognition in real-world images
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
ICDAR 2011 Robust Reading Competition Challenge 2: Reading Text in Scene Images
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Robust Character Recognition Using Connected-Component Extraction
IIH-MSP '11 Proceedings of the 2011 Seventh International Conference on Intelligent Information Hiding and Multimedia Signal Processing
Recognizing natural scene characters by convolutional neural network and bimodal image enhancement
CBDAR'11 Proceedings of the 4th international conference on Camera-Based Document Analysis and Recognition
A Hybrid Approach to Detect and Localize Texts in Natural Scene Images
IEEE Transactions on Image Processing
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Reading text in natural images has focused again the attention of many researchers during the last few years due to the increasing availability of cheap image-capturing devices in low-cost products like mobile phones. Therefore, as text can be found on any environment, the applicability of text-reading systems is really extensive. For this purpose, we present in this paper a robust method to read text in natural images. It is composed of two main separated stages. Firstly, text is located in the image using a set of simple and fast-to-compute features highly discriminative between character and non-character objects. They are based on geometric and gradient properties. The second part of the system carries out the recognition of the previously detected text. It uses gradient features to recognize single characters and Dynamic Programming (DP) to correct misspelled words. Experimental results obtained with different challenging datasets show that the proposed system exceeds state-of-the-art performance, both in terms of localization and recognition.