Hand Sign Gesture Recognition and Translator Using Convolution Neural Networks (CNN) and k-Nearest Neighbors (k-NN)

Sign language serves as a crucial mode of communication for mute and deaf individuals, yet its comprehension is limited due to its specialized nature, posing challenges for effective communication. To address these limitations the Sign Language Recognition and Translation system has been developed employing advanced algorithms including Decision Trees, Convolutional Neural Networks and k-Nearest Neighbors. Additionally, data augmentation techniques were utilized to enhance accuracy and accessibility. Through extensive trials the system demonstrated remarkable accuracy, achieving higher accuracy in translating lengthy phrases containing 8 to 12 words. This level of accuracy indicates the system's capability in analyzing complex and extended sign language motions, thereby providing reliable translations. The Sign Language Recognition and Translation system presented herein offers a promising solution to bridge communication gaps for mute and deaf individuals, signifying a significant step towards fostering inclusivity and accessibility for this community.