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..

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).

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.

A large-scale isolated Indian sign language dataset. It contains 2002 common words, used in daily communications among Indian deaf community. The dataset contains 40033 videos across 2002 words. The total duration of the dataset is around 36.2 hours with 7.8 Million frames.

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.

Dataset

This dataset was created by Vaishnavi Sonawane

Contents

The dataset includes videos files of the hand gestures of eight words (accident, call, doctor, help, hot, lose, pain, thief) from Indian sign language (ISL), commonly used to communicate during emergency situations. The data is useful for the researchers working on vision based automatic sign language recognition as well as hand gesture recognition.

All the words included in the dataset, except the word doctor are dynamic hand gestures. The videos in this dataset were collected by asking the participants to stand comfortably behind a black colored board and present the hand gestures, in front of the board. A Sony cyber shot DSC-W810 digital camera with 20.1 mega pixel resolution has been used for capturing the videos.

The videos have been collected from 26 individuals including 12 males and 14 females in the age group of 22 to 26 years. Two sample videos have been captured from each participant in an indoor environment under normal lighting conditions by placing the camera at a fixed distance. The dataset is presented in two folders with the original raw video sequences in one folder, and the cropped and downsampled video sequences in the other folder.

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).

This dataset consists of static hand gestures and lips movement for each character in the English alphabet, eight Hindi Vowels and ten Numerals as represented in Indian sign language (ISL). The dataset consists of 1,02,470 images of subjects from different age groups presenting static gestures under varied backgrounds and illumination conditions. The dataset is structured into three folders namely Kids, Teenagers and Adults. Each folder consists of sub-folders namely Full Sleeves and Half Sleeves indicating the type of clothing that the subject has worn at the time of image acquisition. In each sub-folders, images for the English alphabet, Hindi Vowels and Numerals are stored respectively in the sub-folders named with that specific character. However, for the English alphabet 'E' and Numeral '9' we have captured two different signs for each (that are used interchangeably), and it is contained in the folder namely E1 and E2 for alphabet 'E' and 9a and 9b for Numeral '9'. For the English alphabet, wherever a character is represented by a dynamic sign, the last frame of the sign is captured. For example, this is typically a case with English characters like 'J', 'H' and 'Y'. The images are stored in .jpeg format and have resolutions varying from 300 x 500 to 800 x 600, and the size is less than 100KB. The dataset is captured by a team pursuing research at Chandigarh College of Engineering and Technology, Chandigarh. The subjects have been informed about the research and Informed Participant Consent has been obtained prior to image acquisition. This dataset can be used only for research purposes either as it is or after cropping the static gestures from the image after duly referencing it. Any other use of the dataset is strictly prohibited and any illegal use is subject to the Indian court of law.

Dataset

This dataset was created by chinmay d

Contents

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).

Dataset

This dataset was created by Adithya S Nair

Contents

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.

Introduction to the Indian Sign Language (ISL) Model

This project focuses on creating a precise and real-time detection model for Indian Sign Language (ISL) gestures. We utilize the YOLO-NAS Object Detection model with the coco/14 checkpoint and leverage Roboflow for data preprocessing, which includes annotating various images and balancing classes. The training, validation, and testing phases are executed using a Google Colab notebook, followed by live detection implementation in PyCharm. This model aims to facilitate seamless communication for the deaf and hard-of-hearing community by translating ISL gestures into text and speech.

The Indian sign language is a book. It was written by William Philo Clark and published by University of Nebraska Press in 1982.

Dataset

This dataset was created by SandeshD419

Released under Apache 2.0

Contents

Video, ELAN, and textual data for Interrogatives in Indian Sign Language. This research has been supported by the German Science Foundation (DFG), grant #STE 958/10-1 / ZE 1040/6-1.

ISL YOLOv5

## Overview

ISL YOLOv5 is a dataset for object detection tasks - it contains Hands annotations for 3,611 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).

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.