A methodology for machine translation of simple sentences from Kannada to English language

Kodabagi, M. M.; Angadi, S. A.

doi:10.1109/ic3i.2016.7917967

Cited by 5 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of the resources suggest a Baseline SMT using MOSES for Kannada-English Translated bible corpus [13] and English-Kannada translation through rule-based MT [14]. Translation for simple sentences is done for Kannada transliterated corpus using lexicon analysis and phrase mapper to identify display boards of Karnataka government offices [15]. In this paper, we have applied the NMT method to translate Kannada Text to English Text using Encoder-Decoder mechanism with LSTM as RNN unit, without attention mechanism.…”

Section: Related Workmentioning

confidence: 99%

Kannada to English Machine Translation Using Deep Neural Network

Nagaraj¹,

Ravikumar²,

Kasyap³

et al. 2021

ISI

View full text Add to dashboard Cite

In this paper, we focus on the unidirectional translation of Kannada text to English text using Neural Machine Translation (NMT). From studies, we found that using Recurrent Neural Network (RNN) has been the most efficient way to perform machine translation.In this process we have used Sequence to Sequence (Seq2Seq) modelled dataset with the help of Encoder-Decoder Mechanism considering Long Short Term Memory (LSTM) as RNN unit. We have compared our result concerning to Statistical Machine Translation (SMT) and obtained a better Bi-Lingual Evaluation Study (BLEU) value, with an accuracy of 86.32%.

show abstract

Section: Related Workmentioning

confidence: 99%

Kannada to English Machine Translation Using Deep Neural Network

Nagaraj¹,

Ravikumar²,

Kasyap³

et al. 2021

ISI

View full text Add to dashboard Cite

show abstract

Tulu Language Text Recognition and Translation

Prathwini,

Rodrigues,

Vijaya

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Language is a primary means of communication, but it is not the only means; knowing a language does, however, assist speed up the process. Many distinct languages are spoken worldwide, and people use them to communicate. This is only one of the many reasons why language is so crucial. Based on the literature survey, it is evident that there is a lack of available translators for the Tulu language. Despite being prevalent predominantly in Karnataka, the Tulu language has not been as widely spoken as other Indian languages until recently, although it gained enough recognition to become the second language in Karnataka. The purpose of our research work aims at translating the English language into the Tulu language. During the evaluation the system was tested on a dataset consisting of handwritten characters during the evaluation process Convolutional Neural Networks used achieved an accuracy rate of 92%. To translate English to the Tulu language, we employed a parallel sentence dataset for the neural approach and a parallel word dataset for the rule-based approach. The rule-based approach resulted in an 89% accuracy rate for word-based analysis and an 81% accuracy rate for sentence-based analysis for the English-to-Tulu language translation. The neural machine translation approach of the Encoder-Decoder model with LSTM is been used to accomplish translation from English to Tulu with a BLEU score of 0.83 and Tulu to English with a BLUE score of 0.65. The model also employed hybrid machine translation to enhance the translation.

show abstract