使用迁移深度学习模型的基于图像的阿拉伯手语识别系统

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Qanita Bani Baker, Nour Alqudah, Tibra Alsmadi, Rasha Awawdeh
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引用次数: 0

摘要

手语是一种独特的沟通工具,有助于弥合听力障碍人士与公众之间的差距。它对各个社区至关重要,因为它使听力有困难的人能够有效地沟通。在手语中,有许多手势,每个手势都有不同的手部形状、手部位置、动作、面部表情和用来传达特定含义的身体部位。视觉手语识别的复杂性对计算机视觉研究领域提出了重大挑战。本文提出了一种利用卷积神经网络(cnn)和多种迁移学习模型自动准确识别阿拉伯手语字符的阿拉伯手语识别系统(ArSL)。本研究使用的数据集包括54,049张ArSL字母图像。本研究结果表明,InceptionV3优于其他预训练模型,在没有过拟合的情况下,实现了100%的准确率得分和0.00的损失得分。这些令人印象深刻的性能指标突出了InceptionV3在识别阿拉伯字符方面的独特能力,并强调了它对过拟合的鲁棒性。这增强了其在阿拉伯语手语识别领域未来研究的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image-Based Arabic Sign Language Recognition System Using Transfer Deep Learning Models
Sign language is a unique communication tool helping to bridge the gap between people with hearing impairments and the general public. It holds paramount importance for various communities, as it allows individuals with hearing difficulties to communicate effectively. In sign languages, there are numerous signs, each characterized by differences in hand shapes, hand positions, motions, facial expressions, and body parts used to convey specific meanings. The complexity of visual sign language recognition poses a significant challenge in the computer vision research area. This study presents an Arabic Sign Language recognition (ArSL) system that utilizes convolutional neural networks (CNNs) and several transfer learning models to automatically and accurately identify Arabic Sign Language characters. The dataset used for this study comprises 54,049 images of ArSL letters. The results of this research indicate that InceptionV3 outperformed other pretrained models, achieving a remarkable 100% accuracy score and a 0.00 loss score without overfitting. These impressive performance measures highlight the distinct capabilities of InceptionV3 in recognizing Arabic characters and underscore its robustness against overfitting. This enhances its potential for future research in the field of Arabic Sign Language recognition.
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来源期刊
Applied Computational Intelligence and Soft Computing
Applied Computational Intelligence and Soft Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
6.10
自引率
3.40%
发文量
59
审稿时长
21 weeks
期刊介绍: Applied Computational Intelligence and Soft Computing will focus on the disciplines of computer science, engineering, and mathematics. The scope of the journal includes developing applications related to all aspects of natural and social sciences by employing the technologies of computational intelligence and soft computing. The new applications of using computational intelligence and soft computing are still in development. Although computational intelligence and soft computing are established fields, the new applications of using computational intelligence and soft computing can be regarded as an emerging field, which is the focus of this journal.
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