Indian Sign Language Dataset

Welcome to the Custom Indian Sign Language Dataset! This dataset has been meticulously curated with the aim of enhancing the accuracy and robustness of ISL gesture recognition systems. By sharing this dataset, we aspire to contribute to the research community and empower fellow researchers and data scientists to explore and innovate in the domain of sign language recognition.

Dataset Overview: This dataset comprises a collection of ISL gesture images, meticulously captured and processed to simulate real-world scenarios. The original images feature a diverse range of gestures performed by myself and a few friends, representing a broad spectrum of sign language expressions.

Controlled Noise Addition: To introduce realism and variability akin to real-world communication scenarios, each image underwent controlled noise addition. This process included the incorporation of various background types such as blurry, messy, and colorful, aiming to emulate dynamic environmental conditions. By intentionally introducing controlled noise, we aimed to equip the dataset with the ability to train models that can effectively handle the challenges posed by different backgrounds and lighting conditions.

Image Specifications: All images in this dataset have been standardized to a size of 126x126 pixels. This uniformity ensures consistency in data presentation and facilitates ease of preprocessing and model development. The images are stored in a format that ensures seamless integration into various machine learning frameworks.

We encourage you to explore, analyze, and leverage this dataset for your research, experimentation, and development endeavors. By utilizing this dataset, you'll gain access to a unique collection of ISL gesture images that have been thoughtfully curated to enhance the accuracy and adaptability of sign language recognition models.

License: Please note that this dataset is provided under the [License Type], which outlines the terms and conditions for its use and distribution. Kindly ensure that you review and adhere to the license specifications when utilizing the dataset.

We invite you to join us in advancing the field of sign language recognition by leveraging this custom dataset as a valuable resource for your research and innovation. Together, let's work towards creating more inclusive and accessible technology solutions for the deaf community.

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.

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).

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 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. This dataset is further modified by adding Hand-Landmarks and Pose-Landmarks and cropping it to fit the specifications of google/vivit-b-16x2

NOTE: Only 80 word signs are uploaded for now, mode word signs will be updated soon.

• Number of Frames per video : 32
• Frame dimensions : 224 X 224 X 3 (height X width X channels)
• Frames per second: 10

Original Dataset can be obtained at: * https://zenodo.org/records/4010759 * https://huggingface.co/datasets/ai4bharat/INCLUDE

Citation [1]A. Sridhar, R. G. Ganesan, P. Kumarand M. Khapra, ‘INCLUDE: A Large Scale Dataset for Indian Sign Language Recognition’. Zenodo, Oct. 16, 2020. doi: 10.1145/3394171.3413528.

ISL Gestures Landmarks Dataset

The dataset contains ISL Gestures for Alphabet classes A-Z, each containing ~2k records. The total entries make up to around 50k records.

Purpose of this dataset

So you have searched all across the web for a good ISL dataset, but didn't find a decent one yet? Did your models also overfit while working on those datasets? well, not anymore! I'm introducing my own dataset.

A Quick History

Other available ISL datasets are in the form of images, not providing enough diversity to even produce a decent model. All the time - at least in my case - models got overfit. Therefore, I've created my own dataset. Tried and tested, and also being my first dataset, it produces a good model!

Features of the Dataset

1. Hand Landmarks are captured rather than images

2. Each class (A-Z) contains ~2,000 records of hand landmarks captured at different angles, rotations & perspectives while maintaining the front view.

3. Additionally, Interchanged hands in single-hand gestures (e.g., C, V, I, L, O, etc.) as well as non-symmetrical gestures (e.g., D, E, F, H, J, etc.) to maintain robustness and add variability

4. The data, after capturing got cleaned enough to provide you the base allowing possibility of further modifications if you want.

5. Feature engineered and produced one column - uses_two_hands - boosting R^2 score to >99% !

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.

The ISL (Indian Sign Language) dataset used for training and evaluating sign language recognition models is typically composed of video samples capturing various hand gestures representing specific words or phrases. This dataset aims to encapsulate the complexity and diversity of ISL, accommodating a wide range of commonly used signs to ensure comprehensive coverage for robust training.

Dataset Composition The ISL dataset generally includes:

Video Samples: Short video clips where signers perform specific signs or sequences of signs. These samples are captured from different perspectives and with varied lighting to improve the model's ability to generalize. Key Landmarks: Each video frame may be annotated or processed to extract key landmarks of the hand (e.g., positions of fingers and joints) using tools like MediaPipe, enabling feature extraction for deep learning. Labels: Each video is labeled with the corresponding word or phrase in ISL, forming the target variable for supervised learning. Features and Variability Gesture Diversity: The dataset covers a range of signs, including those for common nouns, verbs, and everyday expressions. Multiple Signers: To enhance the model's robustness, the dataset often includes recordings from multiple individuals with different hand shapes, signing speeds, and accents in movement. Temporal Information: Each video is processed to maintain the temporal flow of gestures, which is essential for LSTM networks to capture sequential dependencies. Preprocessing and Augmentation To prepare the dataset for training:

Frame Extraction: Video clips are split into frames to create a sequence input for the LSTM model. Landmark Detection: Tools like MediaPipe detect and extract landmarks for each frame, converting video data into structured numerical information. Normalization and Augmentation: The dataset may undergo normalization for scale consistency and data augmentation, such as flipping or rotating frames, to increase variability and improve the model's resilience to noise. Dataset Challenges Complex Hand Movements: Sign languages, including ISL, involve intricate and simultaneous hand motions that require the model to detect fine-grained details. Background Variability: Ensuring consistent backgrounds or handling various backgrounds in training is crucial for model accuracy. Lighting Conditions: The dataset often includes different lighting settings to train the model to adapt to real-world scenarios. The ISL dataset forms the backbone for training the LSTM-driven deep learning model, ensuring that it learns from comprehensive and diverse examples, which contributes to higher recognition accuracy and robust performance in real-world applications.

The dataset contains word-level Indian Sign Language videos in .mp4 format. These signs were sourced from the official Indian Sign Language YouTube channel, maintained by the government body. Four contributors created 60 unique signs, each with 60 corresponding videos. Additionally, 30 videos of still signs were included to improve model accuracy and avoid misinterpretation.

Paper link - https://link.springer.com/chapter/10.1007/978-981-97-6992-6_19

The custom Indian Sign Language (ISL) dataset comprises 26 unique signs, collected using an RGB camera. Each sign class in the dataset is represented by a total of 550 images, providing a comprehensive and balanced representation of the gestures. The dataset serves as a valuable resource for training and evaluating machine learning models for sign language recognition tasks, offering a diverse range of visual data to support the development of accurate and robust sign language recognition systems.

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).

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.

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.

Indian Sign Language Alphabet Dataset: Overview

About the Dataset

The Indian Sign Language Alphabet Dataset is a curated collection of hand gesture images representing the alphabets (A–Z) in Indian Sign Language (ISL). It was specifically created to support the development of Gesture-Speech, a groundbreaking project that translates Indian Sign Language gestures into spoken words, making communication more accessible for the hearing and speech-impaired communities.

The dataset is intended for researchers, developers, and educators to build robust machine learning models for recognizing ISL gestures and advancing assistive technologies.

Project Inspiration: Gesture-Speech

The dataset was developed as part of the Gesture-Speech project, an open-source initiative hosted on GitHub. Gesture-Speech focuses on real-time translation of Indian Sign Language gestures into spoken language using deep learning and computer vision. The project’s mission is to: - Empower differently-abled individuals by breaking communication barriers. - Promote inclusivity through assistive AI-driven technologies. - Enable the use of ISL in everyday interactions.

The dataset serves as the backbone of Gesture-Speech’s training pipeline, allowing the development of accurate and efficient recognition models.

Key Features

1. Dataset Highlights:

   • Classes: 26 classes for each English alphabet (A-Z), represented by corresponding ISL gestures.
   • Images: 1,200 images per class, totaling 31,200 images.
   • Diversity: Includes hand gestures from individuals with varying hand shapes, sizes, and skin tones, ensuring model robustness.
   • Real-world Conditions: Captured in different lighting and background conditions to simulate practical usage scenarios.

2. Integration with Gesture-Speech:

   • Fully compatible with TensorFlow and PyTorch frameworks used in the Gesture-Speech project.
   • Pre-processed and labeled for easy model training and testing.

Dataset Structure

The dataset follows a directory-based structure for classification tasks:

Indian_Sign_Language_Alphabet/
│── a/
│  ├── 0.jpg
│  ├── 1.jpg
│  ├── ...
│── b/
│  ├── 0.jpg
│  ├── 1.jpg
│  ├── ...
│── ...
│── z/

Applications

• Gesture Recognition Models: Train deep learning models (e.g., CNNs) to recognize ISL alphabets.
• Assistive Technology: Power systems like Gesture-Speech to convert gestures into speech for improved accessibility.
• Education Tools: Develop interactive platforms to teach and learn Indian Sign Language.
• Human-Computer Interaction: Enable gesture-controlled applications for robotics and automation.

Technical Details

1. Resolution: Images are resized to 384x384 pixels for consistency.
2. File Format: Images are stored in .jpg format.
3. Dataset Size: 334MB.

Gesture-Speech: GitHub Project

The Gesture-Speech project can be found on GitHub. Key highlights of the project: - Uses TensorFlow and OpenCV to process hand gestures in real-time. - Translates ISL gestures into spoken English or Hindi phrases using a deep learning pipeline. - Open-source and extensible, encouraging contributions and further development.

License

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Users can: - Share: Copy and redistribute the material in any medium or format. - Adapt: Remix, transform, and build upon the material for any purpose, even commercially. - Attribution: Proper credit must be given to the dataset creator.

Acknowledgments

The dataset and the Gesture-Speech project are dedicated to promoting inclusivity and enabling new opportunities for the differently-abled. Special thanks to contributors and volunteers who helped create this dataset by providing diverse samples of ISL gestures.

y

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 to Characters

This dataset provides detailed hand gesture data for recognizing and translating Indian Sign Language (ISL) signs into corresponding text characters. It includes data from both hands and captures key features related to hand movements and finger positions, which are crucial for accurately identifying ISL gestures.

Features:

1. label: The target label representing the corresponding character or symbol for the ISL gesture.
2. both_hands: Indicates if both hands are used in the gesture (1 for yes, 0 for no).
3. thumb_Left: Position of the left thumb during the gesture.
4. index_finger_Left: Position of the left index finger during the gesture.
5. middle_finger_Left: Position of the left middle finger during the gesture.
6. ring_finger_Left: Position of the left ring finger during the gesture.
7. pinky_Left: Position of the left pinky finger during the gesture.
8. palm_angle_Left_left: Left palm's angle in the left direction.
9. palm_angle_Left_right: Left palm's angle in the right direction.
10. hand_Left_ground_angle: Angle of the left hand relative to the ground.
11. thumb_Right: Position of the right thumb during the gesture.
12. index_finger_Right: Position of the right index finger during the gesture.
13. middle_finger_Right: Position of the right middle finger during the gesture.
14. ring_finger_Right: Position of the right ring finger during the gesture.
15. pinky_Right: Position of the right pinky finger during the gesture.
16. palm_angle_Right_left: Right palm's angle in the left direction.
17. palm_angle_Right_right: Right palm's angle in the right direction.
18. hand_Right_ground_angle: Angle of the right hand relative to the ground.

Potential Use Cases:

• Gesture Recognition: Develop machine learning models to translate ISL gestures into text characters.
• Sign Language Interpretation: Build applications to assist in real-time ISL to text translation.
• Hand Gesture Analysis: Study the dynamics of hand movements in Indian Sign Language.

This dataset is a valuable resource for researchers, data scientists, and developers working on gesture recognition, natural language processing (NLP), and assistive technologies for the deaf and hard of hearing community.

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.

LIGHTscrn/Indian-Sign-language-landmarks-30frames dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset contains MP4 video clips of Indian Sign Language (ISL) gestures, intended for use in machine learning, gesture recognition, and accessibility-focused projects. It includes over 3000 short videos featuring alphabets (A–Z), numbers (0–9), and common words like “Hello” and “Thank You.” To meet upload limits, the dataset is split across multiple ZIP files, each with around 1000 videos. All files are in .mp4 format, with consistent resolution and duration. This dataset is useful for building ISL recognition models, real-time sign detection, and inclusive communication tools. Unzip all parts to access the full set.