Sign language is a cardinal element for communication between deaf and dumb community. Sign language has its own grammatical structure and gesticulation nature. Research on SLRT focuses a lot of attention in gesture identification. Sign language comprises of manual gestures performed by hand poses and non-manual features expressed through eye, mouth and gaze movements. The sentence-level completely labelled Indian Sign Language dataset for Sign Language Translation and Recognition (SLTR) research is developed. The ISL-CSLTR dataset assists the research community to explore intuitive insights and to build the SLTR framework for establishing communication with the deaf and dumb community using advanced deep learning and computer vision methods for SLTR purposes. This ISL-CSLTR dataset aims in contributing to the sentence level dataset created with two native signers from Navajeevan, Residential School for the Deaf, College of Spl. D.Ed & B.Ed, Vocational Centre, and Child Care & Learning Centre, Ayyalurimetta, Andhra Pradesh, India and four student volunteers from SASTRA Deemed University, Thanjavur, Tamilnadu. The ISL-CSLTR corpus consists of a large vocabulary of 700 fully annotated videos, 18863 Sentence level frames, and 1036 word level images for 100 Spoken language Sentences performed by 7 different Signers. This corpus is arranged based on signer variants and time boundaries with fully annotated details and it is made available publicly. The main objective of creating this sentence level ISL-CSLRT corpus is to explore more research outcomes in the area of SLTR. This completely labelled video corpus assists the researchers to build framework for converting spoken language sentences into sign language and vice versa. This corpus has been created to address the various challenges faced by the researchers in SLRT and significantly improves translation and recognition performance. The videos are annotated with relevant spoken language sentences provide clear and easy understanding of the corpus data. Acknowledgements: The research was funded by the Science and Engineering Research Board (SERB), India under Start-up Research Grant (SRG)/2019–2021 (Grant no. SRG/2019/001338). And also, we thank all the signers for their contribution in collecting the sign videos and the successful completion of the ISL-CSLTR corpus. We would like to thank Navajeevan, Residential School for the Deaf, College of Spl. D.Ed & B.Ed, Vocational Centre, and Child Care & Learning Centre, Ayyalurimetta, Andhra Pradesh, India for their support and contribution.

Abstract: Indian Sign Language (ISL) is a complete language with its own grammar, syntax, vocabulary and several unique linguistic attributes. It is used by over 5 million deaf people in India. Currently, there is no publicly available dataset on ISL to evaluate Sign Language Recognition (SLR) approaches. In this work, we present the Indian Lexicon Sign Language Dataset - INCLUDE - an ISL dataset that contains 0.27 million frames across 4,287 videos over 263 word signs from 15 different word categories. INCLUDE is recorded with the help of experienced signers to provide close resemblance to natural conditions. A subset of 50 word signs is chosen across word categories to define INCLUDE-50 for rapid evaluation of SLR methods with hyperparameter tuning. The best performing model achieves an accuracy of 94.5% on the INCLUDE-50 dataset and 85.6% on the INCLUDE dataset. Download Instructions: For ease of access, we have prepared a Shell Script to download all the parts of the dataset and extract them to form the complete INCLUDE dataset.You can find the script here: http://bit.ly/include_dl

Indian Sign Language (ISL) is a natural language used by the Deaf community in India for communication. It is a visual-gestural language that relies on a combination of handshapes, facial expressions, and body movements to convey meaning..

This dataset consists of the Indian sign language of all the alphabets and numbers in Indian hand recognition given by ISRTC(Indian Sign Research and Training Center). This dataset is in black white background for faster computing and for getting better accuracy while training the dataset.

Please give credit to this dataset if you download it.

The dataset contains the RGB images of hand gestures of twenty ISL words, namely, ‘afraid’,’agree’,’assistance’,’bad’,’become’,’college’,’doctor’,’from’,’pain’,’pray’, ’secondary’, ’skin’, ’small’, ‘specific’, ‘stand’, ’today’, ‘warn’, ‘which’, ‘work’, ‘you’’ which are commonly used to convey messages or seek support during medical situations. All the words included in this dataset are static. The images were captured from 8 individuals including 6 males and 2 females in the age group of 9 years to 30 years. The dataset contains a 18000 images in jpg format. The images are labelled using the format ISLword_X_YYYY_Z, where: • ISLword corresponds to the words ‘afraid’, ‘agree’, ‘assistance’, ‘bad’, ‘become’, ‘college’, ‘doctor’ ,‘from’, ’pray’, ‘pain’, ‘secondary’, ‘skin’, ‘small’, ‘specific’, ‘stand’, ‘today’, ‘warn’, ‘which’, ‘work’, ‘you’. • X is an image number in the range 1 to 900. • YYYY is an identifier of the participant and is in the range of 1 to 6. • Z corresponds to 01 or 02 that identifies the sample number for each subject. For example, the file named afraid_1_user1_1 is the image sequence of the first sample of the ISL gesture of the word ‘afraid’ presented by the 1st user.

Indian_sign_language_dataset

## Overview

Indian_sign_language_dataset is a dataset for object detection tasks - it contains Objects annotations for 256 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Recent advancements in sign language recognition technology have significantly improved communication for individuals who are deaf or hard of hearing. Despite these advancements, many people who use sign language still face challenges in everyday interactions due to widespread unfamiliarity with sign language. However, new technologies have greatly enhanced our ability to recognize and interpret sign language, making communication more accessible and inclusive.

This dataset includes images of common phrases in both Indian Sign Language (ISL) and American Sign Language (ASL). The images were captured using a standard laptop webcam with a resolution of 680x480 pixels and a bit depth of 24 pixels. The dataset covers 44 different phrases, each represented by 40 images. All images are stored in PNG format. Note that this dataset includes static signs only and does not contain any dynamic sign language gestures.

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Indian Sign Language

## Overview

Indian Sign Language is a dataset for object detection tasks - it contains Hand Signs Indian annotations for 887 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

iSign: A Benchmark for Indian Sign Language Processing

The iSign dataset serves as a benchmark for Indian Sign Language Processing. The dataset comprises of NLP-specific tasks (including SignVideo2Text, SignPose2Text, Text2Pose, Word Prediction, and Sign Semantics). The dataset is free for research use but not for commercial purposes.

  Quick Links

Website: The landing page for iSign arXiv Paper: Detailed information about the iSign Benchmark. Dataset on Hugging Face:… See the full description on the dataset page: https://huggingface.co/datasets/Exploration-Lab/iSign.

Indian Sign Language Detection

## Overview

Indian Sign Language Detection is a dataset for object detection tasks - it contains A Z 0 9 annotations for 1,748 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Recent advancements in sign language recognition technology have significantly improved communication for individuals who are deaf or hard of hearing. Despite these advancements, many people who use sign language still face challenges in everyday interactions due to widespread unfamiliarity with sign language. However, new technologies have greatly enhanced our ability to recognize and interpret sign language, making communication more accessible and inclusive.

This dataset includes images of common phrases in both Indian Sign Language (ISL) and American Sign Language (ASL). The images were captured using a standard laptop webcam with a resolution of 680x480 pixels and a bit depth of 24 pixels. The dataset covers 44 different phrases, each represented by 40 images. All images are stored in PNG format. Note that this dataset includes static signs only and does not contain any dynamic sign language gestures.

Sign languages are the primary means of communication for a large number of people worldwide. Recently, the availability of Sign language translation datasets have facilitated the incorporation of Sign language research in the NLP community. Though a wide variety of research focuses on improving translation systems for sign language, the lack of ample annotated resources hinders research in the data driven natural language processing community. In this resource paper, we introduce ISLTranslate, a translation dataset for continuous Indian Sign Language (ISL), consisting of 30k ISL-English sentence pairs. To the best of our knowledge, it is the first and largest translation dataset for continuous Indian Sign Language with corresponding English transcripts. We provide a detailed analysis of the dataset and examine the distribution of words and phrases covered in the proposed dataset. To validate the performance of existing end-to-end Sign language to spoken language translation systems, we benchmark the created dataset with multiple existing state-of-the-art systems for sign languages.

This dataset contains depth data collected through Intel RealSense Depth Camera D435i. Data corresponding to Indian Sign Language (ISL) gesture of Weekdays (Sunday-Saturday) is used.

Data is stored as comma separated values. Each line corresponds to a sign.

This dataset contains a comprehensive collection of Indian Sign Language (ISL) hand gestures representing the 26 letters of the English alphabet, captured using the MediaPipe pose estimation technology. The dataset includes over 50,000 high-quality images of hand gestures captured from various angles, lighting conditions, and skin tones. The dataset is ideal for researchers and developers working in the field of computer vision, machine learning, and sign language recognition.

Dataset

This dataset was created by Prince Gupta

Contents

Dataset

This dataset was created by Kush

Contents

Dataset Card for Indian Sign Language Recognition

Indian Sign Language Recognition Dataset. 🇮🇳

  Dataset Details





  Dataset Description

Approximately 30K images for Indian Sign Language Recognition.

Curated by: Akshay Bahadur License: MIT

  Capturing your own Dataset

Hand Gestures: For capturing new hand gestures, run the file capture_hand_gestures.py Enter the gesture number to begin capturing the set of photos for that gesture A new folder with the… See the full description on the dataset page: https://huggingface.co/datasets/akshaybahadur21/ISLAR.

Indian Sign Language_40

## Overview

Indian Sign Language_40 is a dataset for object detection tasks - it contains A 8AcR A 8hEz annotations for 1,405 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset tracks annual american indian student percentage from 1988 to 2021 for Milwaukee Sign Language Elementary School vs. Wisconsin and Milwaukee School District