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。