Empirical Performance Evaluation Methodology and Its Application to Page Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Adaptive Segmentation of Document Images
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Delayed reinforcement learning for adaptive image segmentation andfeature extraction
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Automatic localization of page segmentation errors
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
Learning segmentation of documents with complex scripts
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
A semi-automatic adaptive OCR for digital libraries
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
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A hierarchical framework for document segmentation is proposed as an optimization problem. The model incorporates the dependencies between various levels of the hierarchy unlike traditional document segmentation algorithms. This framework is applied to learn the parameters of the document segmentation algorithm using optimization methods like gradient descent and Q-learning. The novelty of our approach lies in learning the segmentation parameters in the absence of groundtruth.