A deep Neural Framework for Continuous sign language recognition by iterative training

Creating a deep neural framework for continuous sign language recognition using iterative training in a paragraph involves several key components.

Framework Overview

The proposed framework leverages a deep learning architecture, typically a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to effectively capture both spatial and temporal features of sign language. The system is designed to recognize continuous signing, where signs are fluid and interconnected, rather than discrete gestures.

Data Acquisition and Preprocessing

To train the model, a diverse dataset of sign language videos is required. These videos are annotated with corresponding sign language glosses. Preprocessing steps include frame extraction, normalization, and data augmentation to enhance robustness against variations in signing styles and backgrounds.

Iterative Training Process

The iterative training process involves several phases:

1. Initial Training: The model is first trained on a baseline dataset to learn general features of sign language.
2. Fine-tuning: After initial training, the model is fine-tuned using a more specific dataset, focusing on particular sign languages or dialects.
3. Continuous Feedback Loop: The model’s performance is continually evaluated on a validation set. Misclassifications lead to adjustments in training data, such as adding more examples of difficult signs or refining annotations.

Evaluation Metrics

The performance of the framework is evaluated using metrics like accuracy, precision, recall, and F1-score. Additionally, a real-time performance assessment is critical for practical applications, ensuring that the model can recognize signs with minimal latency.

Applications

This framework has potential applications in various fields, including education, accessibility, and human-computer interaction, allowing for seamless communication between hearing and deaf communities.

In summary, this deep neural framework aims to create an effective system for continuous sign language recognition through iterative training, enhancing both the model’s accuracy and its applicability in real-world scenarios.