Developing an Intelligent Saudi Sign Language Translator for Inclusive Communication

Hyper-parameter tuning is the process of enhancing a machine learning model's performance by selecting the optimal set of hyper-parameters. These values are defined before the training begins and play a crucial role in guiding the model's learning process. Unlike the model's parameters (such as neural network weights), which are learned during the training phase, hyper-parameters are set manually and significantly impact the model’s accuracy and generalization ability. Benefits of Hyper-Parameter Tuning: Improved Model Performance: Proper tuning helps the model achieve better accuracy and generalization by identifying the most suitable configuration. Prevents Overfitting/Underfitting: Optimizing hyper-parameters such as regularization and model complexity can help avoid overfitting (when a model learns noise) or underfitting (when a model fails to learn). Efficient Resource Use: Well-tuned models make efficient use of computational resources, saving time and re...

This blog provides an in-depth look on the R&D phase about MLP, along with detailed insights from the training and testing phases of the Multilayer Perceptron (MLP) model. In the realm of artificial intelligence, neural networks have become a cornerstone for solving complex problems. One such neural network that has garnered significant attention is the Multilayer Perceptron (MLP). After creating the dataset, we need to have a clear understanding of MLPs. But before we dive into the technical details, let’s first get acquainted with what an MLP truly is. A Multilayer Perceptron is a type of neural network composed of multiple layers, including an input layer, one or more hidden layers, and an output layer. Each of these layers contains neurons, the fundamental building blocks of the network. MLPs are renowned for their versatility and are widely used in tasks like forecasting models and image pattern recognition. [1] Exploring the Architecture and Functioning of a Multilaye...

The whole project development process can be divided into 3 main segments:  • Data generation • Building, training and testing of the model • ROS integration.  The following blog post will be focused on the data generation process. Selecting all the signs to be identified by the model was done and explained in the previous post and this will focus on the development of the program to capture hand landmarks. A brief introduction of the mediapipe library is also included in the post for your reference.  Google Mediapipe:    This is an open source project maintained by google which provides customizable machine learning solutions[1]. The hand detection implementation of media-pipe is used in this project to collect hand information to generate the dataset.MediaPipe Hands is a hand-tracking technology that predicts hand skeletal data using a single RGB camera. Two interdependent models—a palm detector model and a hand landmark model—make up the solution. To identify...