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To the best of our knowledge, this is the first work to perform fully autonomous language grounding in an MR setting for a robot.
The data was tested on 3 Indian scripts for numerals- Hindi, Bengali and Telugu, and 1 script-Hindi for words, the results achieved hence are highly promising.
Considering the complexities of Hindi characters, the technique shows an impressive result using a Multilayer Perceptron MLP based classifier.
We therefore suggest that voice design should be considered more thoroughly when planning spoken human-robot interactions.
Application/Improvements:This application can be helpful in designing robots to understand different forms of Hindi sentences, to use as Hindi tutor for students to get them idea about different form of sentences and in plagiarism tools to find the higher level of plagiarized text up to certain extent.
In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency.
Using spoken captions collected in English and Hindi, we show that the same model architecture can be successfully applied to both languages.