Static Pakistani Sign Language Classification using Support Vector Machine
In this study, a system is proposed that uses the Support Vector Machine (SVM) technique with Bag-of-Words (BoW) and recognizes static Pakistani Sign Language (PSL) alphabets. The application of the BoW technique with SVM, on a PSL images' dataset, has not been performed previously. Similarly,...
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| Format: | Article |
| Language: | English |
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Sir Syed University of Engineering and Technology, Karachi.
2022-12-01
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| Series: | Sir Syed University Research Journal of Engineering and Technology |
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| Online Access: | http://www.sirsyeduniversity.edu.pk/ssurj/rj/index.php/ssurj/article/view/436 |
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| _version_ | 1849434252658081792 |
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| author | Shaheer Mirza Sheikh Muhammad Munaf Shahid Ali Muhammad Asif |
| author_facet | Shaheer Mirza Sheikh Muhammad Munaf Shahid Ali Muhammad Asif |
| author_sort | Shaheer Mirza |
| collection | DOAJ |
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In this study, a system is proposed that uses the Support Vector Machine (SVM) technique with Bag-of-Words (BoW) and recognizes static Pakistani Sign Language (PSL) alphabets. The application of the BoW technique with SVM, on a PSL images' dataset, has not been performed previously. Similarly, no publicly available dataset for PSL is available and previous studies have achieved a maximum classification accuracy of 91.98%. For this study, a total of 511 images are collected for 36 static PSL alphabet signs from a native signer. The Sign Language (SL) recognition system uses the collected images as input and converts them to grayscale. To segment the images, the system uses the thresholding technique and Speeded Up Robust Feature (SURF) to extract the features. The system uses K-means clustering to cluster the extracted features. To form the BoW, the system computes the Euclidean distance among SURF descriptors and clustered data. The system then uses 5-fold cross-validation to divide the codebooks obtained from the BoW into training and testing. The developed system yields an overall accuracy of 97.87% for the classification of static PSL signs at 1,500×1,500 image dimensions and 500 Bags.
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| format | Article |
| id | doaj-art-3dafb2c8b5484866858edcb6a5f49317 |
| institution | Kabale University |
| issn | 1997-0641 2415-2048 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Sir Syed University of Engineering and Technology, Karachi. |
| record_format | Article |
| series | Sir Syed University Research Journal of Engineering and Technology |
| spelling | doaj-art-3dafb2c8b5484866858edcb6a5f493172025-08-20T03:26:43ZengSir Syed University of Engineering and Technology, Karachi.Sir Syed University Research Journal of Engineering and Technology1997-06412415-20482022-12-01122Static Pakistani Sign Language Classification using Support Vector MachineShaheer Mirza0Sheikh Muhammad Munaf1Shahid Ali2Muhammad Asif3Department of Biomedical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, PakistanDepartment of Software Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, PakistanDepartment of Speech Language and Hearing Sciences, Faculty of Health Sciences, Ziauddin University, Karachi, PakistanDepartment of Electrical Engineering, Faculty of Engineering, Science, Technology and Management, Ziauddin University, Karachi, Pakistan In this study, a system is proposed that uses the Support Vector Machine (SVM) technique with Bag-of-Words (BoW) and recognizes static Pakistani Sign Language (PSL) alphabets. The application of the BoW technique with SVM, on a PSL images' dataset, has not been performed previously. Similarly, no publicly available dataset for PSL is available and previous studies have achieved a maximum classification accuracy of 91.98%. For this study, a total of 511 images are collected for 36 static PSL alphabet signs from a native signer. The Sign Language (SL) recognition system uses the collected images as input and converts them to grayscale. To segment the images, the system uses the thresholding technique and Speeded Up Robust Feature (SURF) to extract the features. The system uses K-means clustering to cluster the extracted features. To form the BoW, the system computes the Euclidean distance among SURF descriptors and clustered data. The system then uses 5-fold cross-validation to divide the codebooks obtained from the BoW into training and testing. The developed system yields an overall accuracy of 97.87% for the classification of static PSL signs at 1,500×1,500 image dimensions and 500 Bags. http://www.sirsyeduniversity.edu.pk/ssurj/rj/index.php/ssurj/article/view/436Pattern RecognitionPakistani Sign LanguageMachine LearningImage Processing |
| spellingShingle | Shaheer Mirza Sheikh Muhammad Munaf Shahid Ali Muhammad Asif Static Pakistani Sign Language Classification using Support Vector Machine Sir Syed University Research Journal of Engineering and Technology Pattern Recognition Pakistani Sign Language Machine Learning Image Processing |
| title | Static Pakistani Sign Language Classification using Support Vector Machine |
| title_full | Static Pakistani Sign Language Classification using Support Vector Machine |
| title_fullStr | Static Pakistani Sign Language Classification using Support Vector Machine |
| title_full_unstemmed | Static Pakistani Sign Language Classification using Support Vector Machine |
| title_short | Static Pakistani Sign Language Classification using Support Vector Machine |
| title_sort | static pakistani sign language classification using support vector machine |
| topic | Pattern Recognition Pakistani Sign Language Machine Learning Image Processing |
| url | http://www.sirsyeduniversity.edu.pk/ssurj/rj/index.php/ssurj/article/view/436 |
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