Advanced CNN Arabic Sign Language Detection -- 2

The goal of the project is to create a highly intricate Convolutional Neural Network (CNN) model specifically tailored for Arabic sign language detection from video to text translation. We already have a video dataset available and the codes need to be presented in .ipynb files. Testing accuracy of over 90% is expected.

Your task involves:
- Developing a new model or Enhancing an existing model to develop an advanced (10+ layers) CNN model (or similar model) , proficient in handling Arabic sign language detection.
- Exploiting the Integrated Mediapipe framework for continuous, full sentence video processing.
- Ensuring the system is able to transcribe videos into Arabic text efficiently and accurately.
- Documenting all code clearly and thoroughly throughout the project in addation to some comparisions with other sign language recognation systems results .

Ideal Skills and Experience:
- In-depth knowledge and experience with CNN models and Integrated Mediapipe framework.
- Proficiency in handling .ipynb format.
- Background in video to text translation, specifically in sign language.
- Proven track record of achieving high accuracy rates in previous projects.