The Universal Scholar Journal

Abstract

Phulkari embroidery, a traditional textile art of Punjab, represents a rich cultural heritage characterized by diverse patterns, motifs, and vibrant color compositions. With the growing interest in digital preservation and intelligent textile analysis, the application of machine learning (ML) and deep learning (DL) techniques has emerged as a promising approach for automated Phulkari image analysis and classification. This review paper presents a comprehensive analysis of existing ML and DL methods used in image processing and pattern recognition, with a focus on their applicability to Phulkari embroidery. It explores various feature extraction techniques such as texture, color, and shape descriptors, along with classification algorithms including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Naïve Bayes (NB). The study also highlights the role of convolutional neural networks (CNNs) in learning complex visual patterns directly from images, enabling improved accuracy and efficiency in classification tasks. Furthermore, this review identifies key challenges in Phulkari image analysis, such as limited availability of labeled datasets, high intra-class variation, and similarities between different pattern types. By providing a structured analysis of current methodologies and future research directions, this study aims to guide researchers in developing efficient and intelligent systems for Phulkari image recognition and classification.

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Metric

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Review of Machine and Deep Learning Methods for Phulkari Image Analysis and Classification