Sign language detection dataset: A resource for AI-based recognition systemsMendeley Data
Sign language is a very important mode of communication among deaf and hard-of-hearing populations. Automatic sign language detection based on deep learning model is the theme of this study. Hand gestures are classified by the Convolutional Neural Network (CNN) model to different signs. For training...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925004330 |
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| author | Bindu Garg Manisha Kasar Priyanka Paygude Amol Dhumane Srinivas Ambala Jitendra Rajpurohit Abhay Sharma Vidula Meshram Amber Vats Achyut Kashyap |
| author_facet | Bindu Garg Manisha Kasar Priyanka Paygude Amol Dhumane Srinivas Ambala Jitendra Rajpurohit Abhay Sharma Vidula Meshram Amber Vats Achyut Kashyap |
| author_sort | Bindu Garg |
| collection | DOAJ |
| description | Sign language is a very important mode of communication among deaf and hard-of-hearing populations. Automatic sign language detection based on deep learning model is the theme of this study. Hand gestures are classified by the Convolutional Neural Network (CNN) model to different signs. For training purposes, there are 26,000 images available with 3000 images for every alphabet letter such that there is complete representation of sign language gesture. Photos were taken in controlled lighting with a consistent black background to facilitate better feature extraction. The data contains varied participants of various age groups, skin types, and hand shapes to enhance generalization. Data collection was standardized through iPhone 15 Pro Max, black background cloth, tripod stand, and remote-controlled Drodcam app to maintain consistency in image quality and framing. For diversity and realism, three participants were involved in data collection, each providing 1000 images per sign, resulting in a rich and diverse dataset. Preprocessing of data methods were used for achieving the best quality of data, such as resizing, conversion to grayscale, normalization, and augmentation. Different techniques of data augmentation like rotation, flipping, scaling, brightness change, and addition of Gaussian noise were used to introduce variations in hand gestures and make the model robust against various environmental conditions. The dataset was then partitioned into 70 % training, 15 % validation, and 15 % test sets for maximizing model performance and ensuring good generalization. The dataset show high accuracy, reflecting the potential of the model for real-world usage, such as accessibility tools for the deaf community, educational tools, and real-time sign language recognition systems. |
| format | Article |
| id | doaj-art-8cb897c5945e457696d9f0f10cffbfff |
| institution | DOAJ |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-8cb897c5945e457696d9f0f10cffbfff2025-08-20T03:23:16ZengElsevierData in Brief2352-34092025-08-016111170310.1016/j.dib.2025.111703Sign language detection dataset: A resource for AI-based recognition systemsMendeley DataBindu Garg0Manisha Kasar1Priyanka Paygude2Amol Dhumane3Srinivas Ambala4Jitendra Rajpurohit5Abhay Sharma6Vidula Meshram7Amber Vats8Achyut Kashyap9Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, IndiaBharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India; Corresponding authors.Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India; Corresponding authors.Symbiosis Institute of Technology, Pune, IndiaPimpri Chinchwad College of Engineering, Pune, IndiaSymbiosis Institute of Technology, Pune, IndiaManipal University Jaipur, Jaipur, IndiaVishwakarma Institute of Technology, Pune, IndiaBharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, IndiaBharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, IndiaSign language is a very important mode of communication among deaf and hard-of-hearing populations. Automatic sign language detection based on deep learning model is the theme of this study. Hand gestures are classified by the Convolutional Neural Network (CNN) model to different signs. For training purposes, there are 26,000 images available with 3000 images for every alphabet letter such that there is complete representation of sign language gesture. Photos were taken in controlled lighting with a consistent black background to facilitate better feature extraction. The data contains varied participants of various age groups, skin types, and hand shapes to enhance generalization. Data collection was standardized through iPhone 15 Pro Max, black background cloth, tripod stand, and remote-controlled Drodcam app to maintain consistency in image quality and framing. For diversity and realism, three participants were involved in data collection, each providing 1000 images per sign, resulting in a rich and diverse dataset. Preprocessing of data methods were used for achieving the best quality of data, such as resizing, conversion to grayscale, normalization, and augmentation. Different techniques of data augmentation like rotation, flipping, scaling, brightness change, and addition of Gaussian noise were used to introduce variations in hand gestures and make the model robust against various environmental conditions. The dataset was then partitioned into 70 % training, 15 % validation, and 15 % test sets for maximizing model performance and ensuring good generalization. The dataset show high accuracy, reflecting the potential of the model for real-world usage, such as accessibility tools for the deaf community, educational tools, and real-time sign language recognition systems.http://www.sciencedirect.com/science/article/pii/S2352340925004330American Sign LanguageDeep LearningConvolutional Neural NetworkSign Language Recognition |
| spellingShingle | Bindu Garg Manisha Kasar Priyanka Paygude Amol Dhumane Srinivas Ambala Jitendra Rajpurohit Abhay Sharma Vidula Meshram Amber Vats Achyut Kashyap Sign language detection dataset: A resource for AI-based recognition systemsMendeley Data Data in Brief American Sign Language Deep Learning Convolutional Neural Network Sign Language Recognition |
| title | Sign language detection dataset: A resource for AI-based recognition systemsMendeley Data |
| title_full | Sign language detection dataset: A resource for AI-based recognition systemsMendeley Data |
| title_fullStr | Sign language detection dataset: A resource for AI-based recognition systemsMendeley Data |
| title_full_unstemmed | Sign language detection dataset: A resource for AI-based recognition systemsMendeley Data |
| title_short | Sign language detection dataset: A resource for AI-based recognition systemsMendeley Data |
| title_sort | sign language detection dataset a resource for ai based recognition systemsmendeley data |
| topic | American Sign Language Deep Learning Convolutional Neural Network Sign Language Recognition |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925004330 |
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