This research combines MediaPipe and CNNs for the efficient and accurate
interpretation of ASL dataset for the real-time detection of sign language. The
system presented here captures and processes hands' gestures in real time. the
intended purpose was to create a very easy, accurate, and fast way of entering
commands without the necessity of touching something.MediaPipe supports one of
the powerful frameworks in real-time hand tracking capabilities for the ability
to capture and preprocess hand movements, which increases the accuracy of the
gesture recognition system. Actually, the integration of CNN with the MediaPipe
results in higher efficiency in using the model of real-time processing.The
accuracy achieved by the model on ASL datasets is 99.12\%.The model was tested
using American Sign Language (ASL) datasets. The results were then compared to
those of existing methods to evaluate how well it performed, using established
evaluation techniques. The system will have applications in the communication,
education, and accessibility domains. Making systems such as described in this
paper even better will assist people with hearing impairment and make things
accessible to them. We tested the recognition and translation performance on an
ASL dataset and achieved better accuracy over previous models.It is meant to
the research is to identify the characters that American signs recognize using
hand images taken from a web camera by based on mediapipe and CNNs

基于 MediaPipe 和 CNN 的结合，对手语数据集进行高效准确的实时检测，并且通过对手势进行捕捉和处理，实现了一种无需触摸的命令输入方式，进而提高手势识别系统的准确性。

通过 Mediapipe 和卷积神经网络（CNN）提升手语检测

Enhancing Sign Language Detection through Mediapipe and Convolutional  Neural Networks (CNN)

This research paper describes a realtime system for identifying American Sign
Language (ASL) movements that employs modern computer vision and machine
learning approaches. The suggested method makes use of the Mediapipe library
for feature extraction and a Convolutional Neural Network (CNN) for ASL gesture
classification. The testing results show that the suggested system can detect
all ASL alphabets with an accuracy of 99.95%, indicating its potential for use
in communication devices for people with hearing impairments. The proposed
approach can also be applied to additional sign languages with similar hand
motions, potentially increasing the quality of life for people with hearing
loss. Overall, the study demonstrates the effectiveness of using Mediapipe and
CNN for real-time sign language recognition, making a significant contribution
to the field of computer vision and machine learning.

本研究描述了一种使用现代计算机视觉和机器学习方法的实时美国手语识别系统，该方法使用 Mediapipe 库进行特征提取和卷积神经网络进行手势分类。测试结果显示，该系统能够以 99.95％的准确率检测所有 ASL 字母，表明其在听障人士的通信设备中的潜力。该方法也可以应用于具有类似手势的其他手语，从而提高听力损失者的生活质量。总的来说，本研究证明了使用 Mediapipe 和 CNN 进行实时手语识别的有效性，为计算机视觉和机器学习领域做出了重要贡献。

Mediapipe 和 CNNs 实时美式手语手势识别

Mediapipe and CNNs for Real-Time ASL Gesture Recognition

Sign language recognition could significantly improve the user experience for
d/Deaf people with the general consumer technology, such as IoT devices or
videoconferencing. However, current sign language recognition architectures are
usually computationally heavy and require robust GPU-equipped hardware to run
in real-time. Some models aim for lower-end devices (such as smartphones) by
minimizing their size and complexity, which leads to worse accuracy. This
highly scrutinizes accurate in-the-wild applications. We build upon the SPOTER
architecture, which belongs to the latter group of light methods, as it came
close to the performance of large models employed for this task. By
substituting its original third-party pose estimation module with the MediaPipe
library, we achieve an overall state-of-the-art result on the WLASL100 dataset.
Significantly, our method beats previous larger architectures while still being
twice as computationally efficient and almost $11$ times faster on inference
when compared to a relevant benchmark. To demonstrate our method's combined
efficiency and precision, we built an online demo that enables users to
translate sign lemmas of American sign language in their browsers. This is the
first publicly available online application demonstrating this task to the best
of our knowledge.

该研究利用 SPOTER 架构和 MediaPipe 技术，实现更高效、精准的手语识别，成功创建了首个公开的在线手语翻译应用。

高效精确的手语识别：只需一个优秀的姿态估计库

Combining Efficient and Precise Sign Language Recognition: Good pose  estimation library is all you need

We present a real-time on-device hand tracking pipeline that predicts hand
skeleton from single RGB camera for AR/VR applications. The pipeline consists
of two models: 1) a palm detector, 2) a hand landmark model. It's implemented
via MediaPipe, a framework for building cross-platform ML solutions. The
proposed model and pipeline architecture demonstrates real-time inference speed
on mobile GPUs and high prediction quality. MediaPipe Hands is open sourced at
this https URL

本研究提出了基于单个 RGB 相机的实时手部跟踪流水线用于 AR/VR 应用程序，该流水线包括掌探测器和手部路标模型，使用 MediaPipe 框架实现，可以在移动 GPU 上实现实时推理速度和高精度。