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Review on Hand Written Character Recognition and Speech Recognition Approaches

by Asra Masrat, Deepa Kumari, Hardika Gawde, Mohammed Ammar Makki, Yogita Borse
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 19
Year of Publication: 2022
Authors: Asra Masrat, Deepa Kumari, Hardika Gawde, Mohammed Ammar Makki, Yogita Borse
10.5120/ijca2022922201

Asra Masrat, Deepa Kumari, Hardika Gawde, Mohammed Ammar Makki, Yogita Borse . Review on Hand Written Character Recognition and Speech Recognition Approaches. International Journal of Computer Applications. 184, 19 ( Jun 2022), 27-32. DOI=10.5120/ijca2022922201

@article{ 10.5120/ijca2022922201,
author = { Asra Masrat, Deepa Kumari, Hardika Gawde, Mohammed Ammar Makki, Yogita Borse },
title = { Review on Hand Written Character Recognition and Speech Recognition Approaches },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2022 },
volume = { 184 },
number = { 19 },
month = { Jun },
year = { 2022 },
issn = { 0975-8887 },
pages = { 27-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number19/32429-2022922201/ },
doi = { 10.5120/ijca2022922201 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:21:54.633382+05:30
%A Asra Masrat
%A Deepa Kumari
%A Hardika Gawde
%A Mohammed Ammar Makki
%A Yogita Borse
%T Review on Hand Written Character Recognition and Speech Recognition Approaches
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 19
%P 27-32
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Language is a very essential part of human connection and communication. It enables the flow of thoughts and sharing of ideas and feelings with others. Writing and speaking are the most basic ways to learn a new language. Many platforms utilize different methodologies to facilitate the learning of languages via flashcards, Speech processing, handwritten character recognition, etc. In this paper, we have presented a thorough review of different established or proposed methods for handwritten character recognition, speech processing, Testing mechanisms which could be useful for an AI-enabled language learning platform. The results of the systematic literature review has concluded prominent trends of Deep Learning and Machine Learning approaches which could aid the process of learning a language digitally.

References
  1. A. Mohite and S. Shelke, ”Handwritten Devanagari Character Recog- nition using Convolutional Neural Network,” 2018 4th International Conference for Convergence in Technology (I2CT), 2018, pp. 1-4, doi: 10.1109/I2CT42659.2018.9057991.
  2. I. Dokare, S. Gadge, K. Kharde, S. Bhere and R. Jadhav, ”Recogni- tion of Handwritten Devanagari Character using Convolutional Neural Network,” 2021 3rd International Conference on Signal Processing and Communication (ICPSC), 2021, pp. 353-359, doi: 10.1109/IC- SPC51351.2021.9451716.
  3. Ayushi Y Vadwala, Krina A Suthar, Yesha A Karmakar and Nirali Pandya., ”Survey paper on Different Speech Recognition Algorithm: Challenges and Techniques”, International Journal of Computer Appli- cations 175(1):31-36, October 2017.
  4. S. Boruah and S. Basishtha, ”A study on HMM based speech recog- nition system,” 2013 IEEE International Conference on Computational Intelligence and Computing Research, 2013, pp. 1-5, doi: 10.1109/IC- CIC.2013.6724147.
  5. Y. Gurav, P. Bhagat, R. Jadhav, S. Sinha, “Devanagari Handwritten Char- acter Recognition using Convolutional Neural Networks,” International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 2020.
  6. S. Acharya, A. K. Pant and P. K. Gyawali, “Deep learning based large scale handwritten Devanagari character recognition,” 9th International Conference on Software, Knowledge, Information Management and Applications,(SKIMA), 2015.
  7. Jomy John, ”Support Vector Machine for Handwritten Character Recog- nition,” KKTM Cognizance - A Multidisciplinary Journal, March 2016, ISSN:2456-4168, arXiv:2109.03081
  8. P. P. Nair, A. James and C. Saravanan, ”Malayalam handwritten character recognition using convolutional neural network,” 2017 International Conference on Inventive Communication and Computational Technolo- gies (ICICCT), 2017, pp. 278-281, doi: 10.1109/ICICCT.2017.7975203.
  9. S. Chakraborty and S. Paul, ”Bengali Handwritten Character Transfor- mation: Basic to Compound and Compound to Basic Using Convolu- tional Neural Network,” 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), 2021, pp. 142- 146, doi: 10.1109/ICREST51555.2021.9331247.
  10. Sabato Marco Siniscalchi, Torbjørn Svendsen, Chin-Hui Lee, ”An arti- ficial neural network approach to automatic speech processing,” 2014, Neurocomputing, Volume 140, 2014, Pages 326-338, ISSN 0925-2312.
  11. J. Sutha and N. Ramaraj, ”Neural Network Based Offline Tamil Handwritten Character Recognition System,” International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007), 2007, pp. 446-450, doi: 10.1109/ICCIMA.2007.86.
  12. K. V. Kale, S. V. Chavan, M. M. Kazi and Y. S. Rode, ”Handwritten Devanagari Compound Character Recognition Using Legendre Moment: An Artificial Neural Network Approach,” 2013 International Symposium on Computational and Business Intelligence, 2013, pp. 274-278, doi: 10.1109/ISCBI.2013.62.
  13. W. Ying, ”English Pronunciation Recognition and Detection Based on HMM-DNN,” 2019 11th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), 2019, pp. 648- 652, doi: 10.1109/ICMTMA.2019.00148.
  14. M. Jangid and S. Srivastava, “Handwritten devanagari character recog- nition using layer-wise training of deep convolutional neural networks and adaptive gradient methods,” Journal of Imaging, vol. 4, no. 2, p. 41, 2018.
Index Terms

Computer Science
Information Sciences

Keywords

Convolution Neural Network Hidden-Markov model

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