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Neural Computing and Applications

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01.11.2015 | Original Article

An efficient segmentation technique for Devanagari offline handwritten scripts using the Feedforward Neural Network

verfasst von: Vijay Pal Dhaka, Manoj Kumar Sharma

Erschienen in: Neural Computing and Applications | Ausgabe 8/2015

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Abstract

In this research work, we have proposed a segmentation technique for words and characters of Devanagari offline handwritten scripts. Due to complex structures and high unevenness in writing styles, recognition of words and characters from the unconstrained scripts has become a burning vicinity of interest for researchers. The proposed Pixel Plot and Trace and Re-plot and Re-trace (PPTRPRT) technique extracts text region from Devanagari offline handwritten scripts and lead iterative processes for segmentation of text lines along with skew and de-skew operations. The outcomes of iterations are used in pixel-space-based word segmentation, and the segmented words are used in segmentation of characters. Moreover, PPTRPRT perform various normalization steps to allow deviation in pen breadth and slant in inscription. Investigational outcome shows that the proposed technique is competent to segment characters from Devanagari offline handwritten scripts, and accuracy of outcomes is up to 99.578 %.

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Titel: An efficient segmentation technique for Devanagari offline handwritten scripts using the Feedforward Neural Network
verfasst von: Vijay Pal Dhaka
Manoj Kumar Sharma
Publikationsdatum: 01.11.2015
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 8/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-015-1844-9

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