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Segmentation of handwritten words using structured support vector machine

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Abstract

Words and characters segmentation is a most indispensable and fundamental task for the handwritten script recognition. However, the complex language structures, deviation in pen breadth and slant in inscription make the feature extraction process very challenging. In this research, a binary quadratic process has been formulated for the word segmentation. It deliberates a co-relationship between the inter-word gap and intra-word gap. The structured support vector machine is used for the experiment. Experimental results of public datasets (i.e., ICDAR2009 and ICDAR2013) show state-of-the-art performance of the designed algorithm.

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Authors and Affiliations

  1. Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India

    Manoj Kumar Sharma & Vijaypal Singh Dhaka

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  1. Manoj Kumar Sharma
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  2. Vijaypal Singh Dhaka
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Correspondence to Vijaypal Singh Dhaka.

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Sharma, M.K., Dhaka, V.S. Segmentation of handwritten words using structured support vector machine. Pattern Anal Applic 23, 1355–1367 (2020). https://doi.org/10.1007/s10044-019-00843-x

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