Word Segmentation Method for Handwritten
Documents based on Structured Learning
Abstract— Word Segmentation Method for Handwritten Documents based on Structured Learning. Segmentation of handwritten document imagesinto text-lines and words is an essential task for optical character recognition. However, since the features of handwritten document are irregular and diverse depending on the person, it is considered a challenging problem. In order to address the problem, we formulate the word segmentation problem as a binary quadratic assignment problem that considers pair wise correlations between the gaps as well as the likelihoods of individual gaps. Even though many parameters are involved in our formulation, we estimate all parameters based on the Structured < Final Year Projects 2016 > SVM >framework so that the proposed method works well regardless of writing styles and written languages without user-defined parameters. Experimental results on ICDAR2009/2013handwriting segmentation databases show that proposed method achieves the state-of-the-art performance on Latin-based and Indian languages.
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