Sign Language Prediction Model using Convolution Neural Network.
The barrier between the hearing and the deaf communities in Kenya is a major challenge leading to a major gap in the communication sector where the deaf community is left out leading to inequality. The study used primary and secondary data sources to obtain information about this problem, which incl...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
State Islamic University Sunan Kalijaga
2022-02-01
|
| Series: | IJID (International Journal on Informatics for Development) |
| Subjects: | |
| Online Access: | https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/3284 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849700922427441152 |
|---|---|
| author | Rebeccah Ndungi Samuel Karuga |
| author_facet | Rebeccah Ndungi Samuel Karuga |
| author_sort | Rebeccah Ndungi |
| collection | DOAJ |
| description | The barrier between the hearing and the deaf communities in Kenya is a major challenge leading to a major gap in the communication sector where the deaf community is left out leading to inequality. The study used primary and secondary data sources to obtain information about this problem, which included online books, articles, conference materials, research reports, and journals on sign language and hand gesture recognition systems. To tackle the problem, CNN was used. Naturally captured hand gesture images were converted into grayscale and used to train a classification model that is able to identify the English alphabets from A-Z. Then identified letters are used to construct sentences. This will be the first step into breaking the communication barrier and the inequality. A sign language recognition model will assist in bridging the exchange of information between the deaf and hearing people in Kenya. The model was trained and tested on various matrices where we achieved an accuracy score of a 99% value when run on epoch of 10, the log loss metric returning a value of 0 meaning that it predicts the actual hand gesture images. The AUC and ROC curves achieved a 0.99 value which is excellent. |
| format | Article |
| id | doaj-art-04c0a76f4a8b49c399b1e5fd535bbb10 |
| institution | DOAJ |
| issn | 2252-7834 2549-7448 |
| language | English |
| publishDate | 2022-02-01 |
| publisher | State Islamic University Sunan Kalijaga |
| record_format | Article |
| series | IJID (International Journal on Informatics for Development) |
| spelling | doaj-art-04c0a76f4a8b49c399b1e5fd535bbb102025-08-20T03:18:06ZengState Islamic University Sunan KalijagaIJID (International Journal on Informatics for Development)2252-78342549-74482022-02-011029210110.14421/ijid.2021.32842909Sign Language Prediction Model using Convolution Neural Network.Rebeccah Ndungi0Samuel Karuga1Samasource KenyaComputer Science Department St Paul's University Private bag, limuru - 00217, Kenya The barrier between the hearing and the deaf communities in Kenya is a major challenge leading to a major gap in the communication sector where the deaf community is left out leading to inequality. The study used primary and secondary data sources to obtain information about this problem, which included online books, articles, conference materials, research reports, and journals on sign language and hand gesture recognition systems. To tackle the problem, CNN was used. Naturally captured hand gesture images were converted into grayscale and used to train a classification model that is able to identify the English alphabets from A-Z. Then identified letters are used to construct sentences. This will be the first step into breaking the communication barrier and the inequality. A sign language recognition model will assist in bridging the exchange of information between the deaf and hearing people in Kenya. The model was trained and tested on various matrices where we achieved an accuracy score of a 99% value when run on epoch of 10, the log loss metric returning a value of 0 meaning that it predicts the actual hand gesture images. The AUC and ROC curves achieved a 0.99 value which is excellent.https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/3284classificationkenyan sign languagedeaf communitieshand gesturescommunication gap |
| spellingShingle | Rebeccah Ndungi Samuel Karuga Sign Language Prediction Model using Convolution Neural Network. IJID (International Journal on Informatics for Development) classification kenyan sign language deaf communities hand gestures communication gap |
| title | Sign Language Prediction Model using Convolution Neural Network. |
| title_full | Sign Language Prediction Model using Convolution Neural Network. |
| title_fullStr | Sign Language Prediction Model using Convolution Neural Network. |
| title_full_unstemmed | Sign Language Prediction Model using Convolution Neural Network. |
| title_short | Sign Language Prediction Model using Convolution Neural Network. |
| title_sort | sign language prediction model using convolution neural network |
| topic | classification kenyan sign language deaf communities hand gestures communication gap |
| url | https://ejournal.uin-suka.ac.id/saintek/ijid/article/view/3284 |
| work_keys_str_mv | AT rebeccahndungi signlanguagepredictionmodelusingconvolutionneuralnetwork AT samuelkaruga signlanguagepredictionmodelusingconvolutionneuralnetwork |