Sign Speak: Recogninzing Sign Language with Machine Learning

Abstract

Sign language serves as a critical means of communication for individuals with hearing impairments, enabling them to integrate into society effectively and express themselves. However, interpreting and recognizing sign language gestures present unique challenges due to the dynamic nature of gestures and spatial dependencies inherent in sign language communication. As a response, the SignSpeak project employs advanced machine learning techniques to address these challenges and enhance accessibility for the deaf and hard of hearing community. The project leverages a diverse dataset sourced from Kaggle, comprising images of sign language gestures captured in various contexts. The integration of advanced algorithms, such as 3D Convolutional Neural Networks (CNNs), and Gated Recurrent Units (GRUs), enables SignSpeak to recognize and interpret sign language gestures accurately and in real-time. This integration allows the model to capture both spatial and temporal features inherent in sign language, thus enabling more robust and accurate recognition. The project encompasses several critical stages, including data preprocessing, model development, training, and evaluation. Data preprocessing involves converting the image data into a suitable format and applying augmentation techniques to enhance the diversity and robustness of the dataset. Model development entails designing a deep learning architecture that combines CNNs and GRUs to effectively capture spatial and temporal dependencies in sign language gestures. Training the model involves optimizing parameters and hyperparameters to achieve optimal performance. Evaluation metrics such as accuracy, F1 score, and recall are utilized to assess the model's performance on both training and validation datasets. The trained model is then tested on a separate test dataset to evaluate its real- world performance and generalization ability. Experimental results demonstrate the efficacy of the SignSpeak approach in accurately recognizing and interpreting sign language gestures. The model achieves high accuracy scores, demonstrating its potential to enhance accessibility and inclusion for individuals with hearing impairments. By providing real-time translation of sign language into text or speech, SignSpeak contributes to breaking down communication barriers and promoting equal participation for all members of society.