Identification of Handloom Cloths Using Image Classification Algorithm

This project aims to develop a system that can automatically identify handloom fabrics and detect defective areas in them using deep learning techniques. Specifically, a convolutional neural network (CNN) is employed to classify images of handloom fabrics into different categories, while a deep belief network (DBN) is used to identify defected areas within the classified fabrics. To achieve this, we first collected a large dataset of handloom fabric images, which were labeled into different categories. We then trained a CNN model using this dataset, which achieved good accuracy in classifying handloom fabrics. Next, we applied a DBN algorithm to the classified images to detect defective areas. Overall, our system offers an efficient and automated way of identifying handloom fabrics and detecting defective areas, which can help in improving the quality of handloom products and reduce manual inspection efforts. Then the customers will be known which one is best, the traditional entrepreneurs get the right price.