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.

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 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).

TV

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.

Indian Sign Language

## Overview

Indian Sign Language is a dataset for object detection tasks - it contains Hand Sign Language annotations for 400 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

The Indian Dynamic Sign Language Dataset (IDSLD) is a dynamic gesture video dataset created for Indian Sign Language (ISL) recognition using computer vision and deep learning.

The dataset contains 11 gesture classes with a total of 4,224 videos. Each class includes 384 videos collected from 8 different contributors, with 48 videos per contributor. Videos were recorded under varying camera angles, lighting conditions, hand rotations, zoom levels, and backgrounds to improve diversity and model robustness.

This dataset is suitable for gesture recognition, action recognition, video classification, computer vision, and assistive technology research

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.

This dataset contains 36 classes representing Indian Sign Language (ISL) characters, including digits (0–9) and alphabets (A–Z). Each class has 1,000 images, resulting in a total of 36,000 labeled samples.

The dataset is designed to support research and development in:

Computer Vision: Hand gesture recognition

Deep Learning: Image classification and CNN-based models

Human-Computer Interaction: Enabling communication tools for the deaf and hard-of-hearing community

Sign Language Translation Systems

Dataset Details:

Classes: 36 (0–9, A–Z)

Images per class: 1,000(some might need pre-processing)

Format: JPG

Use cases: Training, validation, and testing of sign language recognition models

This dataset can be used to build, train, and benchmark machine learning models for gesture recognition tasks. It contributes to bridging the communication gap by empowering developers and researchers to create real-world applications such as sign language interpreters, accessibility tools, and educational platforms.

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 the field of… See the full description on the dataset page: https://huggingface.co/datasets/SandhyaPitchika/Hindi-Indian-Sign-language-dataset-ISL.

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IMPORTANT NOTE

The number "0" is not present in the dataset simply because the number "0" in Indian Sign Language is the exact same as the number "0" in American Sign Language (see https://www.kaggle.com/prathumarikeri/american-sign-language-09az ).

Content

My ISL dataset was created by combining my self-made dataset with other small and large datasets found across GitHub and Kaggle.

Acknowledgements

I would like to thank Khaled Jedoui, a Stanford researcher, for helping me compiling this dataset. Without him, many of the inconvenient datasets would still lay embedded deep in inaccessible research papers.

Inspiration

Sign language is still a relatively new and untouched field in regards to utilizing it in machine learning. Thus, datasets are small-scale and hard to utilize.

Indian Sign Language

## Overview

Indian Sign Language is a dataset for object detection tasks - it contains Alphabets Sign annotations for 2,599 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 is an original dataset consisting of around 13,000 images created of Indian Sign Language in real time. This dataset is created as a part of Deep Learning research project on Indian Sign Language translation and recognition.

Content

We refined our images with Gaussian blurring, grayscale conversion, and thresholding to highlight Indian Sign Language gestures. Each alphabet has 500 meticulously processed images, ensuring clarity and depth for future research.

Acknowledgements

This dataset has been meticulously prepared by Nirmitee Sarode, Shreya Rathod and Riva Rodrigues of Sardar Patel Institute of Technology under the expert guidance of our mentor Dr. Dhananjay R. Kalbande.

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 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.

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.

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 Priyaziya

Contents

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.