Sign languages are the primary means of communication for many
hard-of-hearing people worldwide. Recently, to bridge the communication gap
between the hard-of-hearing community and the rest of the population, several
sign language translation datasets have been proposed to enable the development
of statistical sign language translation systems. However, there is a dearth of
sign language resources for the Indian sign language. This resource paper
introduces ISLTranslate, a translation dataset for continuous Indian Sign
Language (ISL) consisting of 31k ISL-English sentence/phrase pairs. To the best
of our knowledge, it is the largest translation dataset for continuous Indian
Sign Language. We provide a detailed analysis of the dataset. To validate the
performance of existing end-to-end Sign language to spoken language translation
systems, we benchmark the created dataset with a transformer-based model for
ISL translation.

ISLTranslate is introduced as the largest translation dataset for continuous Indian Sign Language, consisting of 31k ISL-English sentence/phrase pairs, which is benchmarked against a transformer-based model for ISL translation.

ISLTranslate：印度手语翻译数据集

ISLTranslate: Dataset for Translating Indian Sign Language

People with vocal and hearing disabilities use sign language to express
themselves using visual gestures and signs. Although sign language is a
solution for communication difficulties faced by deaf people, there are still
problems as most of the general population cannot understand this language,
creating a communication barrier, especially in places such as banks, airports,
supermarkets, etc. [1]. A sign language recognition(SLR) system is a must to
solve this problem. The main focus of this model is to develop a real-time
word-level sign language recognition system that would translate sign language
to text. Much research has been done on ASL(American sign language). Thus, we
have worked on ISL(Indian sign language) to cater to the needs of the deaf and
hard-of-hearing community of India[2]. In this research, we provide an Indian
Sign Language-based Sign Language recognition system. For this analysis, the
user must be able to take pictures of hand movements using a web camera, and
the system must anticipate and display the name of the taken picture. The
acquired image goes through several processing phases, some of which use
computer vision techniques, including grayscale conversion, dilatation, and
masking. Our model is trained using a convolutional neural network (CNN), which
is then utilized to recognize the images. Our best model has a 99% accuracy
rate[3].

该研究旨在利用计算机视觉技术和卷积神经网络，开发一种实时、基于印度手语的手语识别系统，以帮助印度的聋人和听力有障碍的人群。经过多次处理和训练，该模型的准确率达到了 99%。

基于图像的印度手语识别：使用深度神经网络的实用综述

Image-based Indian Sign Language Recognition: A Practical Review using  Deep Neural Networks

Deaf individuals confront significant communication obstacles on a daily
basis. Their inability to hear makes it difficult for them to communicate with
those who do not understand sign language. Moreover, it presents difficulties
in educational, occupational, and social contexts. By providing alternative
communication channels, technology can play a crucial role in overcoming these
obstacles. One such technology that can facilitate communication between deaf
and hearing individuals is sign language recognition. We will create a robust
system for sign language recognition in order to convert Indian Sign Language
to text or speech. We will evaluate the proposed system and compare CNN and
LSTM models. Since there are both static and gesture sign languages, a robust
model is required to distinguish between them. In this study, we discovered
that a CNN model captures letters and characters for recognition of static sign
language better than an LSTM model, but it outperforms CNN by monitoring hands,
faces, and pose in gesture sign language phrases and sentences. The creation of
a text-to-sign language paradigm is essential since it will enhance the sign
language-dependent deaf and hard-of-hearing population's communication skills.
Even though the sign-to-text translation is just one side of communication, not
all deaf or hard-of-hearing people are proficient in reading or writing text.
Some may have difficulty comprehending written language due to educational or
literacy issues. Therefore, a text-to-sign language paradigm would allow them
to comprehend text-based information and participate in a variety of social,
educational, and professional settings.
Keywords: deaf and hard-of-hearing, DHH, Indian sign language, CNN, LSTM,
static and gesture sign languages, text-to-sign language model, MediaPipe
Holistic, sign language recognition, SLR, SLT

通过使用卷积神经网络和 LSTM 模型进行手势和静态手语识别，研究开发了一种印度手语转换为文本或语音的文本 - 手语翻译模型，可以帮助聋人和听障人士在社交、教育和职业场境中进行更准确的交流。

使用 Mediapipe Holistic 的印度手语识别

Indian Sign Language Recognition Using Mediapipe Holistic

Communication is defined as the act of sharing or exchanging information,
ideas or feelings. To establish communication between two people, both of them
are required to have knowledge and understanding of a common language. But in
the case of deaf and dumb people, the means of communication are different.
Deaf is the inability to hear and dumb is the inability to speak. They
communicate using sign language among themselves and with normal people but
normal people do not take seriously the importance of sign language. Not
everyone possesses the knowledge and understanding of sign language which makes
communication difficult between a normal person and a deaf and dumb person. To
overcome this barrier, one can build a model based on machine learning. A model
can be trained to recognize different gestures of sign language and translate
them into English. This will help a lot of people in communicating and
conversing with deaf and dumb people. The existing Indian Sing Language
Recognition systems are designed using machine learning algorithms with single
and double-handed gestures but they are not real-time. In this paper, we
propose a method to create an Indian Sign Language dataset using a webcam and
then using transfer learning, train a TensorFlow model to create a real-time
Sign Language Recognition system. The system achieves a good level of accuracy
even with a limited size dataset.

使用机器学习通过识别手势翻译手语对听障人群和其他人之间的交流有重要意义，该研究使用网络摄像头进行印度手语数据集构建，使用迁移学习训练 TensorFlow 模型实现实时手语识别系统，并实现了较高的准确率。