Text segmentation using Gabor filters for automatic document processing
Machine Vision and Applications - Special issue: document image analysis techniques
A Fast Algorithm for Bottom-Up Document Layout Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimax entropy principle and its application to texture modeling
Neural Computation
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
TextFinder: An Automatic System to Detect and Recognize Text In Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parameter-Free Geometric Document Layout Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Page segmentation and classification utilising a bottom-up approach
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Page segmentation using texture analysis
Pattern Recognition
Context-based multiscale classification of document images using wavelet coefficient distributions
IEEE Transactions on Image Processing
Texture classification using spectral histograms
IEEE Transactions on Image Processing
Applying preattentive visual guidance in document image analysis
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
Hi-index | 0.01 |
In this paper, a visual similarity based document layout analysis (DLA) scheme is proposed, which by using clustering strategy can adaptively deal with documents in different languages, with different layout structures and skew angles. Aiming at a robust and adaptive DLA approach, the authors first manage to find a set of representative filters and statistics to characterize typical texture patterns in document images, which is through a visual similarity testing process. Texture features are then extracted from these filters and passed into a dynamic clustering procedure, which is called visual similarity clustering. Finally, text contents are located from the clustered results. Benefit from this scheme, the algorithm demonstrates strong robustness and adaptability in a wide variety of documents, which previous traditional DLA approaches do not possess.