Review on Design Generation, Defect Detection and Identification of Handloom Cloths Using Deep Learning

Traditional textile design known as “handloom design” entails manually weaving elaborate patterns and designs onto a loom. This industry has made a substantial contribution to the textile industry and created several job possibilities. Deep learning systems proved their effectiveness in creating new handloom designs that are visually appealing and distinctive from the old designs. The methodologies offer a way to standardize the design process, save the time and effort needed for the design process, and be flexible enough to keep up with emerging trends. Additionally, an intelligent visual system can also inspect the quality and identify the different handloom fibers with their increasing demand. This paper is being taken down as a way to explain the fundamental design of an automatic system for generating customer-preferred designs along with identification and defect detection of the handloom clothes as well as how it has changed over time in various research projects. It also further distorts our impression of future work on automatic handloom systems.