Shape Based Pakistan Sign Language Categorization Using Statistical Features and Support Vector Machines
All over the world, deaf use sign language to communicate each other. The signs are built and communicated through movements and the shapes of the hands. Pakistan sign language (PSL) is used by the deaf community of Pakistan. Automatic recognition of PSL alphabets into the predefined categories is p...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8480101/ |
| _version_ | 1818557248633307136 |
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| author | Syed Muhammad Saqlain Shah Husnain Abbas Naqvi Javed I. Khan Muhammad Ramzan Zulqarnain Hikmat Ullah Khan |
| author_facet | Syed Muhammad Saqlain Shah Husnain Abbas Naqvi Javed I. Khan Muhammad Ramzan Zulqarnain Hikmat Ullah Khan |
| author_sort | Syed Muhammad Saqlain Shah |
| collection | DOAJ |
| description | All over the world, deaf use sign language to communicate each other. The signs are built and communicated through movements and the shapes of the hands. Pakistan sign language (PSL) is used by the deaf community of Pakistan. Automatic recognition of PSL alphabets into the predefined categories is presented here. The seven categories are defined using the visibility, shape, and the orientations of both the fingers and the hand. The histograms of the uniform local binary (lbp) for the neighboring distance of one, two, and three are computed and concatenated to form a single vector. At a later step, six statistical features of the combined histogram are computed, i.e., standard deviation, variance, skewness, kurtosis, entropy, and energy. The classification is achieved using support vector machines (SVMs). The modality of one-verses-one is used for the adoption of binary SVM into the multi-class SVM. The proposed technique is validated over the data set of 3414 PSL signs, taken through the help of seven native signers. The performance of the proposed technique is evaluated by precision, recall, accuracy, and f-measure, while it is elaborated through the classification matrix, tabular, and graph representations. |
| first_indexed | 2024-12-13T23:57:23Z |
| format | Article |
| id | doaj.art-ec5422689d5e48bcbfb5e42f8e5c94f9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T23:57:23Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ec5422689d5e48bcbfb5e42f8e5c94f92022-12-21T23:26:30ZengIEEEIEEE Access2169-35362018-01-016592425925210.1109/ACCESS.2018.28726708480101Shape Based Pakistan Sign Language Categorization Using Statistical Features and Support Vector MachinesSyed Muhammad Saqlain Shah0https://orcid.org/0000-0003-1274-5168Husnain Abbas Naqvi1Javed I. Khan2Muhammad Ramzan3 Zulqarnain4Hikmat Ullah Khan5https://orcid.org/0000-0002-8178-6652Department of Computer Science and Software Engineering, International Islamic University at Islamabad, Islamabad, PakistanDepartment of Computer Science and Software Engineering, International Islamic University at Islamabad, Islamabad, PakistanDepartment of Computer Science, Kent State University, Kent, OH, USAComputer Science and IT Department, University of Sargodha, Sargodha, PakistanDepartment of Computer Science and Software Engineering, International Islamic University at Islamabad, Islamabad, PakistanDepartment of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantonment, PakistanAll over the world, deaf use sign language to communicate each other. The signs are built and communicated through movements and the shapes of the hands. Pakistan sign language (PSL) is used by the deaf community of Pakistan. Automatic recognition of PSL alphabets into the predefined categories is presented here. The seven categories are defined using the visibility, shape, and the orientations of both the fingers and the hand. The histograms of the uniform local binary (lbp) for the neighboring distance of one, two, and three are computed and concatenated to form a single vector. At a later step, six statistical features of the combined histogram are computed, i.e., standard deviation, variance, skewness, kurtosis, entropy, and energy. The classification is achieved using support vector machines (SVMs). The modality of one-verses-one is used for the adoption of binary SVM into the multi-class SVM. The proposed technique is validated over the data set of 3414 PSL signs, taken through the help of seven native signers. The performance of the proposed technique is evaluated by precision, recall, accuracy, and f-measure, while it is elaborated through the classification matrix, tabular, and graph representations.https://ieeexplore.ieee.org/document/8480101/Pakistan sign languagelocal binary patternmulti-class SVMstatistical features |
| spellingShingle | Syed Muhammad Saqlain Shah Husnain Abbas Naqvi Javed I. Khan Muhammad Ramzan Zulqarnain Hikmat Ullah Khan Shape Based Pakistan Sign Language Categorization Using Statistical Features and Support Vector Machines IEEE Access Pakistan sign language local binary pattern multi-class SVM statistical features |
| title | Shape Based Pakistan Sign Language Categorization Using Statistical Features and Support Vector Machines |
| title_full | Shape Based Pakistan Sign Language Categorization Using Statistical Features and Support Vector Machines |
| title_fullStr | Shape Based Pakistan Sign Language Categorization Using Statistical Features and Support Vector Machines |
| title_full_unstemmed | Shape Based Pakistan Sign Language Categorization Using Statistical Features and Support Vector Machines |
| title_short | Shape Based Pakistan Sign Language Categorization Using Statistical Features and Support Vector Machines |
| title_sort | shape based pakistan sign language categorization using statistical features and support vector machines |
| topic | Pakistan sign language local binary pattern multi-class SVM statistical features |
| url | https://ieeexplore.ieee.org/document/8480101/ |
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