scholarly journals Recognition of South Indian Language Numerals Using Minimum Distance Classifier

2017 ◽  
Vol 14 (4) ◽  
pp. 161-169
Author(s):  
F. Jennifer Alin ◽  
K.N. Saravanan
Keyword(s):  
Minimum Distance ◽  
Indian Language ◽  
South Indian

Narrative and the origins of discourse

2005 ◽  
Vol 19 ◽  
pp. 151-173 ◽  
Author(s):  
David Rose
Keyword(s):  
Language Model ◽  
Functional Language ◽  
Finite Sets ◽  
The South ◽  
Indian Language ◽  
South Indian ◽  
The World ◽  
Discourse Patterns ◽  
Traditional Stories

This paper summarises findings of discourse analyses of traditional stories from eleven language phyla around the world. The aim is a preliminary exploration of relationships amongst diverse languages in patterns of discourse, using a systemic functional language model. Several techniques were developed for managing and displaying the analyses, including translations of the stories, patterns of Theme and participant identities, staging of texts and conjunctive relations between messages, and relations between elements of clauses and between clauses in sequences. These techniques are exemplified with one story from the south Indian language Kodava. Some variations across languages, in strategies for realising these functions are then illustrated. Intriguing commonalities are found in discourse patterns in all the stories, realised by diverse but finite sets of options for grammatical strategies. Finally a map is displayed of relations between discourse features and the discourse systems they realise, and some suggestions are mooted for explaining commonality and diversity.

WAVELET DESCRIPTORS FOR RECOGNITION OF BASIC SYMBOLS IN PRINTED KANNADA TEXT

2007 ◽  
Vol 05 (02) ◽  
pp. 351-367 ◽  
Author(s):  
R. SANJEEV KUNTE ◽  
R. D. SUDHAKER SAMUEL
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
Indian Languages ◽  
Indian Language ◽  
South Indian ◽  
Wavelet Features ◽  
On Line ◽  
Recognition Systems ◽  
System Methodology

Optical Character Recognition (OCR) systems have been effectively developed for the recognition of printed characters of non-Indian languages. Efforts are underway for the development of efficient OCR systems for Indian languages, especially for Kannada, a popular South Indian language. We present in this paper an OCR system developed for the recognition of basic characters in printed Kannada text, which can handle different font sizes and font sets. Wavelets that have been progressively used in pattern recognition and on-line character recognition systems are used in our system to extract the features of printed Kannada characters. Neural classifiers have been effectively used for the classification of characters based on wavelet features. The system methodology can be extended for the recognition of other south Indian languages, especially for Telugu.

Low frequency bi-syllabic wordlists in a South-Indian language, Kannada: development, standardization and validation

2017 ◽  
Vol 15 (1) ◽  
pp. 38-47
Author(s):  
Animesh Barman ◽  
Vijaya Kumar Narne ◽  
Prashanth Prabhu ◽  
Niraj Kumar Singh ◽  
Spoorthi Thammaiah
Keyword(s):  
Low Frequency ◽  
Indian Language ◽  
South Indian

scholarly journals Recognizing Ancient South Indian Language Using Artificial Neural Network Associate With Opposition Based Grey Wolf Optimization Algorithm

2021 ◽  
Author(s):  
A Nareshkumar ◽  
G Geetha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Algorithm ◽  
Test Case ◽  
Grey Wolf ◽  
Indian Languages ◽  
Grey Wolf Optimization ◽  
Indian Language ◽  
South Indian ◽  
Artificial Neural

Abstract Recognizing signs and fonts of prehistoric language is a fairly difficult job that require special tools. This stipulation makes the dispensation period overriding, difficult, and tiresome to calculate. This paper presents a technique for recognizing ancient south Indian languages by applying Artificial Neural Network (ANN) associated with Opposition based Grey Wolf Optimization Algorithm (OGWA). It identifies the prehistoric language, signs and fonts. It is apparent from the ANN system that arbitrarily produced weights or neurons linking various layers plays a significant role in its performance. For adaptively determining these weights, this paper applies various optimization algorithms such as Opposition based Grey Wolf Optimization, Particle Swarm Optimization and Grey Wolf Optimization to the ANN system. Performance results have illustrated that the proposed ANN-OGWO technique achieves superior accuracy over the other techniques. In test case 1, the accuracy value of OGWO is 94.89% and in test case 2, the accuracy value of OGWO is 92.34%, on average, the accuracy of OGWO achieves 5.8% greater accuracy than ANN-GWO, 10.1% greater accuracy than ANN-PSO and 22.1% greater accuracy over conventional ANN technique.

scholarly journals Sentimental analysis using recurrent neural network

2018 ◽  
Vol 7 (2.27) ◽  
pp. 88 ◽  
Author(s):  
Merin Thomas ◽  
Latha C.A
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Learning Approaches ◽  
Short Term ◽  
Indian Language ◽  
Natural Languages ◽  
South Indian ◽  
Learning Technique ◽  
Long Short Term Memory ◽  
Traditional Approaches

Sentiment analysis has been an important topic of discussion from two decades since Lee published his first paper on the sentimental analysis in 2002. Apart from the sentimental analysis in English, it has spread its wing to other natural languages whose significance is very important in a multi linguistic country like India. The traditional approaches in machine learning have paved better accuracy for the Analysis. Deep Learning approaches have gained its momentum in recent years in sentimental analysis. Deep learning mimics the human learning so expectations are to meet higher levels of accuracy. In this paper we have implemented sentimental analysis of tweets in South Indian language Malayalam. The model used is Recurrent Neural Networks Long Short-Term Memory, a deep learning technique to predict the sentiments analysis. Achieved accuracy was found increasing with quality and depth of the datasets. 

Phonetic environment of disfluencies in children with stuttering

2014 ◽  
Vol 0 (0) ◽  
Author(s):  
Sangeetha Mahesh ◽  
Y.V. Geetha
Keyword(s):  
Mother Tongue ◽  
Rank Order ◽  
Relative Difficulty ◽  
Indian Language ◽  
South Indian ◽  
Articulatory Movement ◽  
Time Domains ◽  
Kannada Language ◽  
Picture Description ◽  
Age Range

AbstractPhonological contributions to stuttering have been discussed with increased attention in the recent years. The present study is aimed to analyze the effect of phonological environment during the instances of stuttering. The study included 10 monolingual children with stuttering (CWS) in the age range of 6–8 years, who spoke Kannada (south Indian language) as their mother tongue. Conversation, topic narration, story narration, and picture description tasks were carried out in Kannada language. The relative difficulty of individual syllables for each participant was determined. Further, the effect of phonetic environment (succeeding syllable) during the instances of stuttering was calculated. The results revealed a rank order of relative occurrence of succeeding consonantal contexts. Most of the time, the phonetic environment included voiceless consonants, nasals, and plosives compared to others. This indicated that CWS may have difficulties in the transition of articulatory movement from oral to nasal and from voiced to voiceless consonants during speech production. Findings also revealed variability in the occurrence of phonetic context within and between CWS, which supports the disturbances occurring across various time domains. It is hoped that the findings of the current study will support theorists, researchers, and clinicians in arriving at a more comprehensive understanding of stuttering and phonetic behavior in CWS.

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