Abstract: Hand sign recognition is an innovative application of artificial intelligence that enables machines to interpret and understand human gestures. This project aims to develop an AI-powered Hand Sign Recognition System using deep learning techniques, particularly Convolutional Neural Networks (CNNs). The system will be trained on a dataset of hand gestures, allowing it to accurately classify and recognize different signs in real time.
The project follows a structured workflow, including data collection, preprocessing, model training, and real-time recognition using a webcam. OpenCV is used for image processing, while TensorFlow/Keras handles model training and inference. Transfer learning techniques with pre-trained models such as MobileNetV2 or ResNet50 improve accuracy and efficiency.
The system has applications in sign language interpretation, gesture-based human-computer interaction, and accessibility solutions for differently-abled individuals. Additionally, it can be extended to control devices using hand gestures, enhancing user experience in gaming, virtual reality, and robotics.
By integrating AI with computer vision, this project demonstrates a practical and impactful approach to bridging the gap between human communication and machine understanding.
Keywords: Hand Sign Recognition,Artificial Intelligence (AI) , Deep Learning , Convolutional Neural Networks (CNN) Gesture Recognition , Sign Language Interpretation, Computer Vision , OpenCV , TensorFlow/Keras , Real-Time Processing Human-Computer Interaction (HCI) , Machine Learning , Transfer Learning , Image Classification.
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DOI:
10.17148/IJARCCE.2025.14323
