TextFinder: An Automatic System to Detect and Recognize Text In Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
ICDAR 2003 Robust Reading Competitions
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Automatic Text Location in Images and Video Frames
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Text Detection from Natural Scene Images: Towards a System for Visually Impaired Persons
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Text Locating Competition Results
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A Robust System to Detect and Localize Texts in Natural Scene Images
DAS '08 Proceedings of the 2008 The Eighth IAPR International Workshop on Document Analysis Systems
Linear Time Maximally Stable Extremal Regions
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Scene Text Recognition Using Similarity and a Lexicon with Sparse Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text Localization in Natural Scene Images Based on Conditional Random Field
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Scene Text Extraction Using Focus of Mobile Camera
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Detecting and reading text in natural scenes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A new class of learnable detectors for categorisation
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Automatic detection and recognition of signs from natural scenes
IEEE Transactions on Image Processing
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Multi-script and multi-oriented text localization from scene images
CBDAR'11 Proceedings of the 4th international conference on Camera-Based Document Analysis and Recognition
A head-mounted device for recognizing text in natural scenes
CBDAR'11 Proceedings of the 4th international conference on Camera-Based Document Analysis and Recognition
Scene text detection using graph model built upon maximally stable extremal regions
Pattern Recognition Letters
Large-lexicon attribute-consistent text recognition in natural images
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Object reading: text recognition for object recognition
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
MAPS: midline analysis and propagation of segmentation
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Recognition of Kannada characters extracted from scene images
Proceeding of the workshop on Document Analysis and Recognition
A text reading algorithm for natural images
Image and Vision Computing
Accurate and robust text detection: a step-in for text retrieval in natural scene images
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Scale based region growing for scene text detection
Proceedings of the 21st ACM international conference on Multimedia
Text extraction from natural scene image: A survey
Neurocomputing
Integrating multiple character proposals for robust scene text extraction
Image and Vision Computing
Transform invariant text extraction
The Visual Computer: International Journal of Computer Graphics
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A general method for text localization and recognition in real-world images is presented. The proposed method is novel, as it (i) departs from a strict feed-forward pipeline and replaces it by a hypothesesverification framework simultaneously processing multiple text line hypotheses, (ii) uses synthetic fonts to train the algorithm eliminating the need for time-consuming acquisition and labeling of real-world training data and (iii) exploits Maximally Stable Extremal Regions (MSERs) which provides robustness to geometric and illumination conditions. The performance of the method is evaluated on two standard datasets. On the Char74k dataset, a recognition rate of 72% is achieved, 18% higher than the state-of-the-art. The paper is first to report both text detection and recognition results on the standard and rather challenging ICDAR 2003 dataset. The text localization works for number of alphabets and the method is easily adapted to recognition of other scripts, e.g. cyrillics.