Abstract
A boosted tree classifier is proposed to segment machine printed, handwritten and overlapping text from documents with handwritten annotations. Each node of the tree-structured classifier is a binary weak learner. Unlike a standard decision tree (DT) which only considers a subset of training data at each node and is susceptible to over-fitting, we boost the tree using all available training data at each node with different weights. The proposed method is evaluated on a set of machine-printed documents which have been annotated by multiple writers in an office/collaborative environment. The experimental results show that the proposed algorithm outperforms other methods on an imbalanced data set.
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