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

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.

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.

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 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.

TV

The dataset includes 150+animated videos of indian sign language. The dimensions of the videos are 1280*720 with similar background. This dataset was created in response to a project on ISL. The videos are created using blender software. The dataset was created using the signs demonstrated on Indian Sign Language Research and Training Centre website which took as reference and created the dataset with 150+videos for Indian sign language.

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.

As the creator of this project, I developed this dataset to address a major gap in the field of Accessibility and Computer Vision. While many resources exist for American Sign Language (ASL), there is a significant lack of standardized data for regional Indian languages.

This is India’s first comprehensive image dataset specifically curated for Hindi characters in Indian Sign Language (ISL). My goal is to empower researchers and developers to create localized solutions that help bridge the communication gap for the deaf and hard-of-hearing community in India.

Dataset Composition Target Language: Hindi (Devanagari Script). Sign Language: Indian Sign Language (ISL). Format: High-resolution RGB images. Classes: [Insert number of characters, e.g., 36] distinct Hindi alphabets and numerals. Total Images: [Insert total count, e.g., 12,000+] samples. Why I created this Existing sign language models often fail to recognize the unique nuances of ISL. By providing the first-ever open-source repository for Hindi ISL gestures, I hope to spark innovation in: Real-time Translation: Converting hand signs into Hindi text or speech. Educational Apps: Helping children learn the Hindi alphabet through interactive ISL tools. Human-Computer Interaction: Enabling gesture-based controls for regional software.

Here is a professionally formatted README.md file designed for your Hugging Face dataset. This file is optimized to help you rank on search engines while clearly linking back to your Kaggle repository.

  Indian Sign Language: Hindi (ISL-Hindi)







  India’s First Comprehensive Image Dataset for Hindi Character Recognition








  Overview

This dataset is the first of its kind, specifically curated to address the lack of resources for regional Indian languages in… See the full description on the dataset page: https://huggingface.co/datasets/SandhyaPitchika/Hindi-Indian-Sign-language-dataset-ISL.

Indian Sign Language

## Overview

Indian Sign Language is a dataset for object detection tasks - it contains Objects GWim annotations for 472 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).

organised alphabetically by the first letter of the English word

This video and gloss-based dataset has been meticulously crafted to enhance the precision and resilience of ISL (Indian Sign Language) gesture recognition and generation systems. Our goal in sharing this dataset is to contribute to the research community, providing a valuable resource for fellow researchers to explore and innovate in the realm of sign language recognition and generation.Overview of the Dataset: Comprising a diverse array of ISL gesture videos and gloss datasets. The term "gloss" in this context often refers to a written or spoken description of the meaning of a sign, allowing for the representation of sign language in a written form. The dataset includes information about the corresponding spoken or written language and the gloss for each sign. Key components of a sign language gloss dataset include ISL grammar that follows a layered approach, incorporating specific spatial indices for tense and a lexicon with compounds. It follows a unique word order based on noun, verb, object, adjective, or part of a question. Marathi sign language follows the subject-object-verb (SOV) form, facilitating comprehension and adaptation to regional languages. This Marathi sign language gloss aims to become a medium for everyday communication among deaf individuals. This dataset reflects a careful curation process, simulating real-world scenarios. The original videos showcase a variety of gestures performed by a professional signer capturing a broad spectrum of sign language expressions. Incorporating Realism with green screen with controlled lighting conditions. All videos within this dataset adhere to pixels, ensuring uniformity for data presentation and facilitating streamlined pre-processing and model development stored in a format compatible with various machine and Deep learning frameworks, these videos seamlessly integrate into the research pipeline

This paper presents a multimodal dataset for static Indian Sign Language (ISL) hand signs encompassing English and Marathi alphabets as well as numeric digits. Each sign is represented in three index-aligned formats: (1) original RGB hand gesture images, (2) pose-extracted skeletal keypoints generated using MediaPipe [1], and (3) skin-segmented binary masks produced with OpenCV [2]. The dataset covers 79 sign classes — 26 English letters (A–Z), 43 Marathi letters (अ–ज्ञ), and 10 digits (0–9) — collected from four participants against a uniform background under consistent indoor lighting using an iPhone 15 in square format, with all images resized to 1280 × 1280 pixels. Each class contains exactly 20 images per modality, yielding 1,580 images per modality and 4,740 images in total, derived from 1,425 unique photographic captures; for classes with fewer than 20 unique captures, existing images were duplicated to equalize class sizes, and all such entries are explicitly flagged in the accompanying manifest. To our knowledge, this is the first ISL dataset to include bilingual English–Marathi alphabet gestures with pose and skin-segmented modalities. The dataset is designed for training and benchmarking machine learning models for ISL recognition, comparative studies across image modalities, and the development of assistive technologies, with the aim of facilitating inclusive gesture recognition research and multilingual accessibility.

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.

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The Indian Sign Language (ISL) Video and Gloss Dataset is a multimodal benchmark designed to facilitate research in sign language recognition, translation, and generation. The dataset consists of professionally recorded ISL videos paired with corresponding English, Marathi, and ISL gloss annotations, providing a rich resource for developing data-driven models that bridge communication between the Deaf and hearing communities. The videos were recorded under controlled studio conditions using a green-screen background and consistent lighting to ensure high visual quality and minimize environmental variations. By combining synchronized visual and textual information, the dataset supports a wide range of applications, including Continuous Sign Language Recognition (CSLR), Sign Language Translation (SLT), Sign Language Generation (SLG), gesture recognition, computer vision, multimodal learning, and deep learning research.

Disclaimer: The original owners/authors (Prachi Waghmare, Ashwini Deshpande) of the dataset completely reserve the right to contact us regarding the removal of the hosted dataset.

Sign Language Recognition is a breakthrough for helping deaf-mute people and has been researched for many years. It is still a challenge for deaf and dumb people to normally have a conversation since most people aren’t acquainted with sign language. But owing to recent technology, we are better able to understand people using sign language. This dataset contains images of the most common phrases in Indian Sign Language. The images of ISL are captured using a standard webcam of a laptop with a resolution of 1280*720. These include 30 images of 40 different signs from ISL. Images are not filtered or resized, are captured under varying light intensities and different backgrounds, and stored in NumPy arrays. These images are of static sign language phrases and do not include any dynamic sign.

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.

Dataset

This dataset was created by Juli Gopani

Released under Apache 2.0

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