Communication is an essential part of day-to-day life. However, people who have disabling hearing loss, which in general is known as deafness, or have a severe loss of hearing use sign language for their communication. Those people learn this language through their respective schools, whereas hearing people rarely get to know this communication tool, leaving a wide communication gap. To bridge this gap, an automated communication system that can convert text to sign language without depending on human interpreters. This literature review overviews sign language and discusses the different technologies used for text-to-sign conversion systems. The paper evaluates computer-based text processing methods, machine translation techniques (rule-based, corpus-based, and neural architectures), and sign synthesis approaches (avatar animation, video concatenation, neural generation). The paper compares each method's strengths and limitations and presents quantitative performance data from recent systems. The review also provides a comprehensive summary of widely used datasets and introduces a visual taxonomy of translation approaches. Furthermore, the paper discusses the current limitations, including scalability, data limitations, the need for more expressive synthesis, and future scope for improvement. The expected outcome of this review is to find and gather information on various sign language systems worldwide, identify research gaps, and propose further enhancements in text-to-sign language conversion.

Incorporating deep learning models has marked a significant advancement in integrating trends and technology within the fashion industry. These models are extensively applied in the realm of image recognition, product recommendation, and trend prediction, employing deep learning techniques such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), and Autoencoders. This paper aims to cover various aspects of the textile industry’s supply chain processes, highlighting these deep learning techniques' present influence and potential future directions. It includes a comprehensive analysis of some of the most recent and well-recognized studies in the industry that focus on different parts of a product’s lifecycle in the industry, such as Design and Trend Forecasting, Production and Quality Control, Marketing and Sales, and Distribution and Retail. While deep learning has significantly improved the efficiency of processes across the supply chain, our review highlights some of the existing challenges, such as dependency on large datasets, manual annotation needs, and limitations in creative design generation, encouraging future research to focus on more sophisticated models incorporating multimodal data and personalized factors like body types and aesthetic preferences. Additionally, areas like sewing pattern generation, body-aware designs, and ethical sourcing are critical areas of the fashion industry that require further exploration.