Sign Language Interpretation using Ensembled Deep Learning Models
Communication is an integral part of our day-to-day lives. People experiencing difficulty in speaking or hearing often feel neglected in our society. While Automatic Speech Recognition Systems have now progressed to the purpose of being commercially viable, Signed Language Recognition Systems are st...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2023/03/itmconf_icdsia2023_01003.pdf |
| _version_ | 1797807646430986240 |
|---|---|
| author | Khanna Samarth Nagpal Kabir |
| author_facet | Khanna Samarth Nagpal Kabir |
| author_sort | Khanna Samarth |
| collection | DOAJ |
| description | Communication is an integral part of our day-to-day lives. People experiencing difficulty in speaking or hearing often feel neglected in our society. While Automatic Speech Recognition Systems have now progressed to the purpose of being commercially viable, Signed Language Recognition Systems are still in the early stages. Currently, all such interpretations are administered by humans. Here, we present an approach using ensembled architecture for the classification of Sign Language characters. The novel ensemble of InceptionV3 and ResNet101 achieved an accuracy of 97.24% on the ASL dataset. |
| first_indexed | 2024-03-13T06:25:45Z |
| format | Article |
| id | doaj.art-6c2c3c1f4569457c90fa7462e3bdfa70 |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-03-13T06:25:45Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-6c2c3c1f4569457c90fa7462e3bdfa702023-06-09T09:24:03ZengEDP SciencesITM Web of Conferences2271-20972023-01-01530100310.1051/itmconf/20235301003itmconf_icdsia2023_01003Sign Language Interpretation using Ensembled Deep Learning ModelsKhanna Samarth0Nagpal Kabir1Manipal University JaipurManipal University JaipurCommunication is an integral part of our day-to-day lives. People experiencing difficulty in speaking or hearing often feel neglected in our society. While Automatic Speech Recognition Systems have now progressed to the purpose of being commercially viable, Signed Language Recognition Systems are still in the early stages. Currently, all such interpretations are administered by humans. Here, we present an approach using ensembled architecture for the classification of Sign Language characters. The novel ensemble of InceptionV3 and ResNet101 achieved an accuracy of 97.24% on the ASL dataset.https://www.itm-conferences.org/articles/itmconf/pdf/2023/03/itmconf_icdsia2023_01003.pdf |
| spellingShingle | Khanna Samarth Nagpal Kabir Sign Language Interpretation using Ensembled Deep Learning Models ITM Web of Conferences |
| title | Sign Language Interpretation using Ensembled Deep Learning Models |
| title_full | Sign Language Interpretation using Ensembled Deep Learning Models |
| title_fullStr | Sign Language Interpretation using Ensembled Deep Learning Models |
| title_full_unstemmed | Sign Language Interpretation using Ensembled Deep Learning Models |
| title_short | Sign Language Interpretation using Ensembled Deep Learning Models |
| title_sort | sign language interpretation using ensembled deep learning models |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2023/03/itmconf_icdsia2023_01003.pdf |
| work_keys_str_mv | AT khannasamarth signlanguageinterpretationusingensembleddeeplearningmodels AT nagpalkabir signlanguageinterpretationusingensembleddeeplearningmodels |