Visual ontology construction for digitized art image retrieval
Journal of Computer Science and Technology
Detection of text region and segmentation from natural scene images
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Text detection in images based on color texture features
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Studying digital imagery of ancient paintings by mixtures of stochastic models
IEEE Transactions on Image Processing
Text Extraction and Document Image Segmentation Using Matched Wavelets and MRF Model
IEEE Transactions on Image Processing
A comprehensive method for multilingual video text detection, localization, and extraction
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, we present a method to extract the inscription area in Traditional Chinese Painting (TCP) images. Our proposed algorithm analyzes the existing text extraction algorithms for natural image and combines the features of inscription in the TCP images. The first step is to extract the ink area in TCP images. The second step is to select the connected area within the scope. The third step is to detect and label the corner points. The last step is to extract the inscription area by using the information of seal and corner points in the TCP image. 1000 images are used in our experiments to verify the effectiveness of the proposed algorithm. Experimental results are provided to demonstrate the effectiveness of the proposed method on our image set.