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Design of Sign Language Recognition Using E-CNN

机译：使用E-CNN设计手语识别

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摘要

Most people do not understand sign language, so they need a bridge for the community to be able to communicate with deaf people. Technology that continues to develop and continues to strive to help humans, can be a solution that can be used to create a communication bridge between the community and deaf people, the use of technology that can be used is the use of image processing technology as a translator tool. Image processing can translate images into text. In the implementation of digital image processing, it will use the hand key point library, where the hand key point library is a library that will detect the location of the hand in each image, but as it is known, image processing cannot stand alone as a data processor but requires an algorithm that functions as a classification tool. The Convolutional Neural Network (CNN) algorithm in the Deep Learning method can be a classification tool, with the ability of the Convolutional Neural Network (CNN) to learn several things. And according to several previous studies that combining several algorithms can increase the accuracy value. In this study, a trial of combining CNN models using the Ensemble method has been successfully carried out with the results being able to increase the accuracy value to 99.4%. So that the results of the research can be summarized that using Ensemble can increase the higher accuracy value.
机译：大多数人都不理解手语，所以他们需要一座桥为社区能够与聋人沟通。继续发展和继续努力帮助人类的技术可以是一个解决方案，可以用于在社区和聋人之间创建一个通信桥，可以使用的技术是使用图像处理技术作为一个翻译工具。图像处理可以将图像转换为文本。在数字图像处理的实现中，它将使用手键点库，其中手键点库是将检测每个图像中手的位置的库，而是知道，图像处理不能单独站立数据处理器，但需要用作分类工具的算法。深度学习方法中的卷积神经网络（CNN）算法可以是分类工具，具有卷积神经网络（CNN）学习几件事的能力。并且根据以前的几项研究，结合多种算法可以提高精度值。在本研究中，通过能够将精度值提高至99.4％的结果，成功地进行了使用集合方法结合CNN模型的试验。因此，可以概述研究结果可以概括，使用集合可以提高更高的精度值。

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