Sign Language Detection
This work addresses sign language detection for accessibility applications, but it is incremental as it applies existing methods without major innovations.
The paper tackled sign language detection by proposing two models: one using ORB and SVM for feature extraction and classification, and another using a CNN architecture, with the CNN model also converted to tflite format for Android development.
With the advancements in Computer vision techniques the need to classify images based on its features have become a huge task and necessity. In this project we proposed 2 models i.e. feature extraction and classification using ORB and SVM and the second is using CNN architecture. The end result of the project is to understand the concept behind feature extraction and image classification. The trained CNN model will also be used to convert it to tflite format for Android Development.