IEEE Transactions on Pattern Analysis and Machine Intelligence
TextFinder: An Automatic System to Detect and Recognize Text In Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Text Detection for Video Analysis
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
Automatic Text Extraction from Video for Content-Based Annotation and Retrieval
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Text Enhancement with Asymmetric Filter for Video OCR
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
ICDAR 2003 Robust Reading Competitions
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
Scene Text Extraction in Natural Scene Images using Hierarchical Feature Combining and Verification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Optimization Design of Cascaded Classifiers
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Text Locating Competition Results
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
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
A region growing and merging algorithm to color segmentation
Pattern Recognition
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
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
On cluster-wise fuzzy regression analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Mathematical and Computer Modelling: An International Journal
Classification of handprinted Kanji characters by the structured segment matching method
Pattern Recognition Letters
End-to-end scene text recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Detection and recognition of text superimposed in complex background has been considered as a challenging problem. Most of the existing methods first locate the text regions and then feed them into OCR package for recognition. However, these methods cannot achieve good recognition performance due to the complex background. For this purpose, this paper proposes a novel text detection and recognition method by using color clustering to divide images into multiple layers according to main color class. In the proposed method, we exploited a connected component analysis to obtain the candidate text regions from each color layer, and then a cascade Adaboost classifier is adopted to determine whether the candidate text regions is real text regions in the corresponding image layer. Because the monochrome color exists in each layer, the interference of the background can be effectively reduced, which can significantly improve the accuracy of text regions localization. Afterwards, an OCR package is used to recognize the text regions which have been located by the cascade Adaboost classifier. Since the text region has a monochrome color, it helps to greatly improve the recognition rate. Finally, the relationship between different layers is used to verify the recognition results by the text location. The experimental results show that the proposed approach significantly outperforms the existing methods.