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This paper presents SignNet, a novel ensemble model of convolutional neural networks (CNNs) designed for recognizing fingerspelled American Sign Language (ASL) signs. The model incorporates scale space theory and consists of low, intermediate, and high-resolution networks using augmented VGG-16 architectures, achieving over 92% accuracy on a synthetic dataset. The study highlights the challenges of sign language recognition and the effectiveness of the proposed approach in improving classification performan

Author
gayitthoi02
Language
EN

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