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This final year project aims to bridge the communication gap for the deaf and voiceless community through a sign language recognition system. Using a dataset of approximately 29,000 images, the project compares K-Nearest Neighbors (KNN), Multi-class Support Vector Machine (SVM), and Convolutional Neural Network (CNN) algorithms to recognize hand gestures. The dataset is preprocessed and split into training and testing sets, achieving accuracy rates of 93.83% (KNN), 88.89% (SVM), and 98.49% (CNN). This IEEE-based project is ideal for computer science students seeking innovative solutions to enhance accessibility and communication.
Upon purchasing this project online, you will receive recorded video tutorials, comprehensive documentation, complete frontend (HTML, CSS, JavaScript) and backend (Python with KNN, SVM, CNN implementations) codes, reports, PPTs, and datasets, all sent automatically to your email. For further support, contact our technical team at +91 8088605682. Please note that once payment is made, no refunds will be issued under any circumstances.
Receive fully functional and tested code for sign language recognition, implemented using Python with KNN, SVM, and CNN algorithms, including a user-friendly interface for gesture recognition.
Get detailed documentation, including reports, PPTs, and datasets for research papers, sent automatically to your email upon purchase.
For personalized guidance on installation and implementation, contact our technical team at +91 8088605682 to arrange one-to-one online sessions (additional charges apply).
For project customization or additional feature integration, contact our technical team at +91 8088605682 to discuss your requirements (additional charges apply).
For hands-on, in-person assistance, contact our team at +91 8088605682 to arrange offline support at our Bangalore center (additional charges apply).
This is one of the best IEEE Machine Learning project ideas for final-year students. The project includes complete frontend (HTML, CSS, JavaScript) and backend (Python with KNN, SVM, CNN implementations) codes, along with detailed explanations to ensure thorough understanding. Smart AI Technologies offers thorough guidance, complete support in implementing the sign language recognition system, and content for your report and IEEE paper publication, making it a commercially viable solution for enhancing accessibility and communication.