Text Identification in Noisy Document Images Using Markov Random Field
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Text Location in Images and Video Frames
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
On the selection of an optimal wavelet basis for texture characterization
IEEE Transactions on Image Processing
A spatial-temporal approach for video caption detection and recognition
IEEE Transactions on Neural Networks
Color text extraction with selective metric-based clustering
Computer Vision and Image Understanding
Color-based clustering for text detection and extraction in image
Proceedings of the 15th international conference on Multimedia
Instant tactile-audio map: enabling access to digital maps for people with visual impairment
Proceedings of the 11th international ACM SIGACCESS conference on Computers and accessibility
Fast and independent access to map directions for people who are blind
Interacting with Computers
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Text detection in color images has become an active research area since recent decades. In this paper, we present a novel approach to accurately detect text in color images possibly with a complex background. First, we use an elaborate edge detection algorithm to extract all possible text edge pixels. Second connected component analysis is employed to construct text candidate region and classify part non-text regions. Third each text candidate region is verified with texture features derived from wavelet domain. Finally, the Expectation maximization algorithm is introduced to binarize text regions to prepare data for recognition. In contrast to previous approach, our algorithm combines both the efficiency of connected component based method and robustness of texture based analysis. Experimental results show that our algorithm is robust in text detection with respect to different character size, orientation, color and language and can provide reliable text binarization result.