Page segmentation and classification
CVGIP: Graphical Models and Image Processing
Skew correction of document images using interline cross-correlation
CVGIP: Graphical Models and Image Processing
The skew angle of printed documents
Document image analysis
Segmentation of page images using the area Voronoi diagram
Computer Vision and Image Understanding - Special issue on document image understanding and retrieval
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Document Spectrum for Page Layout Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and Inferring Image Segmentations using the GBP Typical Cut Algorithm
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Learning to segment document images
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Performance comparison of six algorithms for page segmentation
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
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Most of the state-of-the-art segmentation algorithms are designed to handle complex document layouts and backgrounds, while assuming a simple script structure such as in Roman script. They perform poorly when used with Indian languages, where the components are not strictly collinear. In this paper, we propose a document segmentation algorithm that can handle the complexity of Indian scripts in large document image collections. Segmentation is posed as a graph cut problem that incorporates the apriori information from script structure in the objective function of the cut. We show that this information can be learned automatically and be adapted within a collection of documents (a book) and across collections to achieve accurate segmentation. We show the results on Indian language documents in Telugu script. The approach is also applicable to other languages with complex scripts such as Bangla, Kannada, Malayalam, and Urdu.