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Image approach to english digits recognition using deep learning

Despite good progress in speech recognition, various challenges still exist due to differences in how they speak, age, gender, emotions, and dialects when perceived by the ear. There is a proverb “I hear, and I forget; I see, and I remember”. The image would be another solution to recognize what we...

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Main Authors: Fatin Nur Amalina, Zainol, Mohd Zamri, Ibrahim
Format: Conference or Workshop Item
Language:English
English
Published: Institution of Engineering and Technology 2022
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/41957/1/Image%20approach%20to%20english%20digits%20recognition%20using%20deep%20learning.pdf
http://umpir.ump.edu.my/id/eprint/41957/2/Image%20approach%20to%20english%20digits%20recognition%20using%20deep%20learning_ABS.pdf
http://umpir.ump.edu.my/id/eprint/41957/
https://doi.org/10.1049/icp.2022.2484
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spelling my.ump.umpir.419572024-08-30T00:24:01Z http://umpir.ump.edu.my/id/eprint/41957/ Image approach to english digits recognition using deep learning Fatin Nur Amalina, Zainol Mohd Zamri, Ibrahim T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Despite good progress in speech recognition, various challenges still exist due to differences in how they speak, age, gender, emotions, and dialects when perceived by the ear. There is a proverb “I hear, and I forget; I see, and I remember”. The image would be another solution to recognize what we hear. The main objective of this paper is to investigate the graphic method to learn digit English using the Deep Learning technique. In this work, Mel-frequency cepstral coefficients (MFCC) in the form of an image will be used as input to the system. Convolutional neural network (CNN) will be used to extract features from the image and an artificial neural network (ANN) will be used to classify those features into 10-digit English classes. By using the Speech Command dataset, the performance of the system will be compared with a conventional method that uses MFCC features in the form of a signal. The experiments showed that the image approach improves the recognition rate from 49% to 84%. It can be concluded that image approach can be used as an alternative method for digit recognition. Institution of Engineering and Technology 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41957/1/Image%20approach%20to%20english%20digits%20recognition%20using%20deep%20learning.pdf pdf en http://umpir.ump.edu.my/id/eprint/41957/2/Image%20approach%20to%20english%20digits%20recognition%20using%20deep%20learning_ABS.pdf Fatin Nur Amalina, Zainol and Mohd Zamri, Ibrahim (2022) Image approach to english digits recognition using deep learning. In: IET Conference Proceedings. 2022 Engineering Technology International Conference, ETIC 2022 , 7 - 8 September 2022 , Kuantan, Virtual. pp. 6-11., 2022 (22). ISSN 2732-4494 ISBN 978-183953782-0 (Published) https://doi.org/10.1049/icp.2022.2484
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Fatin Nur Amalina, Zainol
Mohd Zamri, Ibrahim
Image approach to english digits recognition using deep learning
description Despite good progress in speech recognition, various challenges still exist due to differences in how they speak, age, gender, emotions, and dialects when perceived by the ear. There is a proverb “I hear, and I forget; I see, and I remember”. The image would be another solution to recognize what we hear. The main objective of this paper is to investigate the graphic method to learn digit English using the Deep Learning technique. In this work, Mel-frequency cepstral coefficients (MFCC) in the form of an image will be used as input to the system. Convolutional neural network (CNN) will be used to extract features from the image and an artificial neural network (ANN) will be used to classify those features into 10-digit English classes. By using the Speech Command dataset, the performance of the system will be compared with a conventional method that uses MFCC features in the form of a signal. The experiments showed that the image approach improves the recognition rate from 49% to 84%. It can be concluded that image approach can be used as an alternative method for digit recognition.
format Conference or Workshop Item
author Fatin Nur Amalina, Zainol
Mohd Zamri, Ibrahim
author_facet Fatin Nur Amalina, Zainol
Mohd Zamri, Ibrahim
author_sort Fatin Nur Amalina, Zainol
title Image approach to english digits recognition using deep learning
title_short Image approach to english digits recognition using deep learning
title_full Image approach to english digits recognition using deep learning
title_fullStr Image approach to english digits recognition using deep learning
title_full_unstemmed Image approach to english digits recognition using deep learning
title_sort image approach to english digits recognition using deep learning
publisher Institution of Engineering and Technology
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/41957/1/Image%20approach%20to%20english%20digits%20recognition%20using%20deep%20learning.pdf
http://umpir.ump.edu.my/id/eprint/41957/2/Image%20approach%20to%20english%20digits%20recognition%20using%20deep%20learning_ABS.pdf
http://umpir.ump.edu.my/id/eprint/41957/
https://doi.org/10.1049/icp.2022.2484
_version_ 1822924599440965632
score 13.232414

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