NORMA eResearch @NCI Library

Creation of Mnemonics for Hindi alphabets using CNN and Autoencoders

-, Palak (2020) Creation of Mnemonics for Hindi alphabets using CNN and Autoencoders. Masters thesis, Dublin, National College of Ireland.

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Mnemonic helps the brain in retaining memory via visual, audio, textual or any other means. The use of Mnemonics is a comparably lesser explored method for language learning, even though it is fairly effective. The research generates visual mnemonics for the Hindi language using machine learning algorithms to make Hindi character learning stimulating for learners. The creation of mnemonics is a tiresome process; hence this research enabled the algorithms to create visual mnemonics for learners instead. The research used Convolutional Neural Network (CNN) for classification of handwritten Hindi characters and Autoencoders for feature extraction of characters as well as potential mnemonic images. The entire research is divided into four related stages, each with its own objectives. CNN gave an accuracy of 98.48% and autoencoder had MSE score of 0.038. The images generated by the autoencoder weren’t entirely visible for normal eyes, hence they were evaluated using Euclidean distance with the help of nearest neighbours algorithm. The resultant images were suggestions that could work as mnemonics; however, it depends on the individual to validate the impact of any of the suggested images.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 18 Jan 2021 15:29
Last Modified: 18 Jan 2021 15:29

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