Hand Gesture Recognition System for Arabic Letters and Numbers Using Deep Learning
| المصدر: | مجلة بحوث جامعة تعز - سلسلة الآداب والعلوم الإنسانية والتطبيقية |
|---|---|
| الناشر: | جامعة تعز |
| المؤلف الرئيسي: | Saeed, Mogeeb A. (Author) |
| مؤلفين آخرين: | Almourish, Mohammed Hashem (Co-Author) , Saeed, Ahmed Y. A. (Co-Author) |
| المجلد/العدد: | ع28 |
| محكمة: | نعم |
| الدولة: |
اليمن |
| التاريخ الميلادي: |
2021
|
| الشهر: | سبتمبر |
| الصفحات: | 66 - 71 |
| رقم MD: | 1261852 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | EduSearch, AraBase, HumanIndex |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Hand Gesture Recognition | Deep Learning | Convolution Neural Network
|
| رابط المحتوى: |
الناشر لهذه المادة لم يسمح بإتاحتها. |
| المستخلص: |
Recognizing hand gestures is a key to overcome many of the difficulties people with a physical disability have in communicating with the general public. Therefore, it was necessary to develop a technology that solves this problem and enables people with disabilities to communicate with people without problems. This study presents a hand gesture recognition system to recognize Arabic numbers and letters using artificial intelligence that uses deep learning technology. The convolution neural network (CNN) was used as a deep learning model to train data sets on hand gestures, where the gesture images were displayed on the network entrance and changed Scale the images to the same size to extract the image features and then categorize them into text. The results showed that the CNN model achieved an accuracy of 99% in the testing phase. |
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