Sign Language Recognition—Dataset Cleaning for Robust Word Classification in a Landmark-Based Approach
Communication barriers between hard-of-hearing and hearing individuals can be mitigated through advancements in sign language recognition (SLR) systems. These SLR systems can also improve the user experience of hard-of-hearing people when interacting with conversational systems that could emerge in...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10981774/ |
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| author | Pawel Antonowicz David Kasperek Michal Podpora |
| author_facet | Pawel Antonowicz David Kasperek Michal Podpora |
| author_sort | Pawel Antonowicz |
| collection | DOAJ |
| description | Communication barriers between hard-of-hearing and hearing individuals can be mitigated through advancements in sign language recognition (SLR) systems. These SLR systems can also improve the user experience of hard-of-hearing people when interacting with conversational systems that could emerge in the near future. This work explores a landmark-based approach for word classification within an SLR system. The study investigates the impact of a novel data-cleaning methodology on model performance during training. Specifically, a data cleaning process focused on video trimming and sign placement correction is shown to significantly improve dataset quality, resulting in more accurate classification. This cleaner data not only facilitated a more stable training process for the RNN model but also effectively delayed the onset of overfitting compared to a model trained on the original data. The findings highlight the critical role of data quality, particularly when dealing with the limitations inherent to small datasets commonly encountered in SLR tasks. The contribution of this study lies in demonstrating how targeted data cleaning enhances model stability and performance in resource-limited SLR systems. |
| format | Article |
| id | doaj-art-19e8175ebff6453cbc93814347e7aa46 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-19e8175ebff6453cbc93814347e7aa462025-08-20T02:31:02ZengIEEEIEEE Access2169-35362025-01-0113818778188810.1109/ACCESS.2025.356633810981774Sign Language Recognition—Dataset Cleaning for Robust Word Classification in a Landmark-Based ApproachPawel Antonowicz0https://orcid.org/0000-0002-7405-8745David Kasperek1https://orcid.org/0000-0001-5659-0933Michal Podpora2https://orcid.org/0000-0002-1080-6767Department of Computer Science, Opole University of Technology, Opole, PolandDepartment of Computer Science, Opole University of Technology, Opole, PolandInstitute of Computer Science, University of Opole, Opole, PolandCommunication barriers between hard-of-hearing and hearing individuals can be mitigated through advancements in sign language recognition (SLR) systems. These SLR systems can also improve the user experience of hard-of-hearing people when interacting with conversational systems that could emerge in the near future. This work explores a landmark-based approach for word classification within an SLR system. The study investigates the impact of a novel data-cleaning methodology on model performance during training. Specifically, a data cleaning process focused on video trimming and sign placement correction is shown to significantly improve dataset quality, resulting in more accurate classification. This cleaner data not only facilitated a more stable training process for the RNN model but also effectively delayed the onset of overfitting compared to a model trained on the original data. The findings highlight the critical role of data quality, particularly when dealing with the limitations inherent to small datasets commonly encountered in SLR tasks. The contribution of this study lies in demonstrating how targeted data cleaning enhances model stability and performance in resource-limited SLR systems.https://ieeexplore.ieee.org/document/10981774/Sign language recognitionconversational systemdeep learningrecurrent neural networksLSTMdataset cleaning |
| spellingShingle | Pawel Antonowicz David Kasperek Michal Podpora Sign Language Recognition—Dataset Cleaning for Robust Word Classification in a Landmark-Based Approach IEEE Access Sign language recognition conversational system deep learning recurrent neural networks LSTM dataset cleaning |
| title | Sign Language Recognition—Dataset Cleaning for Robust Word Classification in a Landmark-Based Approach |
| title_full | Sign Language Recognition—Dataset Cleaning for Robust Word Classification in a Landmark-Based Approach |
| title_fullStr | Sign Language Recognition—Dataset Cleaning for Robust Word Classification in a Landmark-Based Approach |
| title_full_unstemmed | Sign Language Recognition—Dataset Cleaning for Robust Word Classification in a Landmark-Based Approach |
| title_short | Sign Language Recognition—Dataset Cleaning for Robust Word Classification in a Landmark-Based Approach |
| title_sort | sign language recognition x2014 dataset cleaning for robust word classification in a landmark based approach |
| topic | Sign language recognition conversational system deep learning recurrent neural networks LSTM dataset cleaning |
| url | https://ieeexplore.ieee.org/document/10981774/ |
| work_keys_str_mv | AT pawelantonowicz signlanguagerecognitionx2014datasetcleaningforrobustwordclassificationinalandmarkbasedapproach AT davidkasperek signlanguagerecognitionx2014datasetcleaningforrobustwordclassificationinalandmarkbasedapproach AT michalpodpora signlanguagerecognitionx2014datasetcleaningforrobustwordclassificationinalandmarkbasedapproach |