Skew correction of document images using interline cross-correlation
CVGIP: Graphical Models and Image Processing
TextFinder: An Automatic System to Detect and Recognize Text In Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Caption Localization in Compressed Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
ICDAR 2003 Robust Reading Competitions
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convex hull based skew estimation
Pattern Recognition
Color-based clustering for text detection and extraction in image
Proceedings of the 15th international conference on Multimedia
Character-Stroke Detection for Text-Localization and Extraction
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Geometric Rectification of Camera-Captured Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hough transform based fast skew detection and accurate skew correction methods
Pattern Recognition
A stroke filter and its application to text localization
Pattern Recognition Letters
Fixed-Point Continuation for $\ell_1$-Minimization: Methodology and Convergence
SIAM Journal on Optimization
Video text detection based on filters and edge features
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Fast and robust text detection in images and video frames
Image and Vision Computing
An efficient method for text detection in video based on stroke width similarity
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Proceedings of the 11th European conference on Computer vision: Part I
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Detecting and reading text in natural scenes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Text Detection Using Edge Gradient and Graph Spectrum
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
New Wavelet and Color Features for Text Detection in Video
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
TILT: transform invariant low-rank textures
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
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
Automatic text detection and tracking in digital video
IEEE Transactions on Image Processing
Text String Detection From Natural Scenes by Structure-Based Partition and Grouping
IEEE Transactions on Image Processing
End-to-end scene text recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Automatically extracting texts from natural images is very useful for many applications such as augmented reality. Most of the existing text detection systems require that the texts to be detected (and recognized) in an image are taken from a nearly frontal viewpoint. However, texts in most images taken naturally by a camera or a mobile phone can have a significant affine or perspective deformation, making the existing text detection and the subsequent OCR engines prone to failures. In this paper, based on stroke width transform and texture invariant low-rank transform, we propose a framework that can detect and rectify texts in arbitrary orientations in the image against complex backgrounds, so that the texts can be correctly recognized by common OCR engines. Extensive experiments show the advantage of our method when compared to the state of art text detection systems.