Web-Based Cotton Fabric Pattern Classification using Deep Convolutional Neural Networks (CNN)
Keerti
, Megha , Deeksha , Dr. Anita Harsoor
Keywords — Feature extraction, CNN features, cotton classification, pattern recognition.
Abstract — Identifying fabric patterns in the textile
industry is a critical task, but relying on manual process is
often a bottleneck. It’s a slow, repetitive process that is not
only exhausting for workers but also prone to simple human
errors. To fix this, we have developed an automated system
that uses a Convolutional Neural Network (CNN) to take
the guesswork out of cotton fabric classification. Through a
streamlined web interface, we enable users to upload fabric
photos for immediate evaluation. The system automatically
enhances image clarity before employing deep learning
models to extract distinctive pattern characteristics. By
moving away from inconsistent manual inspections, our
approach generates a reliable confidence score that ensures
faster and more precise industrial verification.
"Web-Based Cotton Fabric Pattern Classification using Deep Convolutional Neural Networks (CNN)", JETNR - JOURNAL OF EMERGING TRENDS AND NOVEL RESEARCH (www.JETNR.org), ISSN:2984-9276, Vol.4, Issue 6, page no.a466-a474, June-2026, Available :https://rjpn.org/JETNR/papers/JETNR2606061.pdf
Volume 4
Issue 6,
June-2026
Pages : a466-a474
Paper Reg. ID: JETNR_234996
Published Paper Id: JETNR2606061
Downloads: 00063
Research Area: Science and Technology
Country: Gulbarga, Karnataka, India
ISSN: 2984-9276 | IMPACT FACTOR: 9.87 Calculated By Google Scholar | ESTD YEAR: 2023
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.87 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: RJPN (IJPublication) Janvi Wave
