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A Combined Architecture Based on Artificial Neural Network to Recognize Kannada Vowel Modifiers

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1037))

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Abstract

The document image analysis for Indian scripts such as Kannada poses many challenges due to particular characteristics of the script. The Kannada script has huge set of characters consists of vowels, consonants, consonant conjuncts. The character set also includes compound characters which are formed using the basic symbols. A typical procedure to perform Kannada character recognition is to segment words and characters from the document then carry out recognition. But Kannada has the larger character set, and such an approach will have many classes to recognize. Another method is to segment the character into basic symbols and then perform recognition of the basic symbols. The glyph corresponding to a Kannada character has mainly two parts: consonant and vowel modifiers. This paper, a combined architecture is proposed to perform recognition of Kannada vowel modifiers. Gabor filters are employed to carry out precise segmentation of character into basic symbols. A combined architecture using K-Mean clustering and Artificial Neural Network is developed to recognize segmented vowel modifiers. The 10-fold cross validation is performed and an overall recognition rate of 95.04% is observed.

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Authors and Affiliations

  1. Department of Computer Science, Birla Institute of Technology and Science, Pilani-Dubai, Dubai, UAE

    Siddhaling Urolagin

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  1. Siddhaling Urolagin
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Correspondence to Siddhaling Urolagin .

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Editors and Affiliations

  1. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Solapur University, Solapur, India

    Ravindra S. Hegadi

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Urolagin, S. (2019). A Combined Architecture Based on Artificial Neural Network to Recognize Kannada Vowel Modifiers. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1037. Springer, Singapore. https://doi.org/10.1007/978-981-13-9187-3_19

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  • DOI: https://doi.org/10.1007/978-981-13-9187-3_19

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9186-6

  • Online ISBN: 978-981-13-9187-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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