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Probabilistic random field based method for annotated machine

Probabilistic random field based method for annotated machine 2025

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The dissertation by Xujun Peng presents a novel method for preprocessing annotated machine-printed documents using probabilistic random fields. It addresses the challenges of separating handwritten annotations from machine-printed text, particularly in documents captured with handheld devices under poor lighting conditions. The research introduces a three-part system that includes binarization of document images, initial classification using boosted tree classifiers, and a Markov random field (MRF) based relabeling approach to improve segmentation accuracy. The proposed methods demonstrate significant improvements in text retrieval and recognition tasks, especially for mixed-content documents.

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