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

  • Siddhaling UrolaginEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)


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.


Combined classifier Vowel recognition Kannada document image analysis 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceBirla Institute of Technology and Science, Pilani-DubaiDubaiUAE

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