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From our experience of large-scale English-Hindi MT, we are convinced that fluency and fidelity in the Hindi output get an order of magnitude facelift if accurate case markers and suffixes are produced.
In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency.
During simulations and evaluation, the accuracy up to 91.30% is achieved, which is significantly better in comparison to other existing approaches for Hindi parts of speech tagging.
Considering the complexities of Hindi characters, the technique shows an impressive result using a Multilayer Perceptron MLP based classifier.
Using spoken captions collected in English and Hindi, we show that the same model architecture can be successfully applied to both languages.

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