Handloomed fabrics recognition with deep learning
Abstract Every nation treasures its handloom heritage, and in India, the handloom industry safeguards cultural traditions, sustains millions of artisans, and preserves ancient weaving techniques. To protect this legacy, a critical need arises to distinguish genuine handloom products, exemplified by...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-04-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-58750-z |
| _version_ | 1797219957091598336 |
|---|---|
| author | Lipi B. Mahanta Deva Raj Mahanta Taibur Rahman Chandan Chakraborty |
| author_facet | Lipi B. Mahanta Deva Raj Mahanta Taibur Rahman Chandan Chakraborty |
| author_sort | Lipi B. Mahanta |
| collection | DOAJ |
| description | Abstract Every nation treasures its handloom heritage, and in India, the handloom industry safeguards cultural traditions, sustains millions of artisans, and preserves ancient weaving techniques. To protect this legacy, a critical need arises to distinguish genuine handloom products, exemplified by the renowned “gamucha” from India’s northeast, from counterfeit powerloom imitations. Our study’s objective is to create an AI tool for effortless detection of authentic handloom items amidst a sea of fakes. Six deep learning architectures—VGG16, VGG19, ResNet50, InceptionV3, InceptionResNetV2, and DenseNet201—were trained on annotated image repositories of handloom and powerloom towels (17,484 images in total, with 14,020 for training and 3464 for validation). A novel deep learning model was also proposed. Despite respectable training accuracies, the pre-trained models exhibited lower performance on the validation dataset compared to our novel model. The proposed model outperformed pre-trained models, demonstrating superior validation accuracy, lower validation loss, computational efficiency, and adaptability to the specific classification problem. Notably, the existing models showed challenges in generalizing to unseen data and raised concerns about practical deployment due to computational expenses. This study pioneers a computer-assisted approach for automated differentiation between authentic handwoven “gamucha”s and counterfeit powerloom imitations—a groundbreaking recognition method. The methodology presented not only holds scalability potential and opportunities for accuracy improvement but also suggests broader applications across diverse fabric products. |
| first_indexed | 2024-04-24T12:41:53Z |
| format | Article |
| id | doaj.art-c77cd5eca2084f62b3892c4a563438b9 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T12:41:53Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-c77cd5eca2084f62b3892c4a563438b92024-04-07T11:14:07ZengNature PortfolioScientific Reports2045-23222024-04-0114111710.1038/s41598-024-58750-zHandloomed fabrics recognition with deep learningLipi B. Mahanta0Deva Raj Mahanta1Taibur Rahman2Chandan Chakraborty3Mathematical and Computational Sciences Division, Institute of Advanced Study in Science & Technology (IASST) (An Autonomous R&D Institute Under Department of Science & Technology)Mathematical and Computational Sciences Division, Institute of Advanced Study in Science & Technology (IASST) (An Autonomous R&D Institute Under Department of Science & Technology)Mathematical and Computational Sciences Division, Institute of Advanced Study in Science & Technology (IASST) (An Autonomous R&D Institute Under Department of Science & Technology)Department of Computer Science and Engineering, NITTTRAbstract Every nation treasures its handloom heritage, and in India, the handloom industry safeguards cultural traditions, sustains millions of artisans, and preserves ancient weaving techniques. To protect this legacy, a critical need arises to distinguish genuine handloom products, exemplified by the renowned “gamucha” from India’s northeast, from counterfeit powerloom imitations. Our study’s objective is to create an AI tool for effortless detection of authentic handloom items amidst a sea of fakes. Six deep learning architectures—VGG16, VGG19, ResNet50, InceptionV3, InceptionResNetV2, and DenseNet201—were trained on annotated image repositories of handloom and powerloom towels (17,484 images in total, with 14,020 for training and 3464 for validation). A novel deep learning model was also proposed. Despite respectable training accuracies, the pre-trained models exhibited lower performance on the validation dataset compared to our novel model. The proposed model outperformed pre-trained models, demonstrating superior validation accuracy, lower validation loss, computational efficiency, and adaptability to the specific classification problem. Notably, the existing models showed challenges in generalizing to unseen data and raised concerns about practical deployment due to computational expenses. This study pioneers a computer-assisted approach for automated differentiation between authentic handwoven “gamucha”s and counterfeit powerloom imitations—a groundbreaking recognition method. The methodology presented not only holds scalability potential and opportunities for accuracy improvement but also suggests broader applications across diverse fabric products.https://doi.org/10.1038/s41598-024-58750-zTextile loom typeHandloom fabricPowerloom fabricAutomated identificationArtificial intelligenceDeep learning |
| spellingShingle | Lipi B. Mahanta Deva Raj Mahanta Taibur Rahman Chandan Chakraborty Handloomed fabrics recognition with deep learning Scientific Reports Textile loom type Handloom fabric Powerloom fabric Automated identification Artificial intelligence Deep learning |
| title | Handloomed fabrics recognition with deep learning |
| title_full | Handloomed fabrics recognition with deep learning |
| title_fullStr | Handloomed fabrics recognition with deep learning |
| title_full_unstemmed | Handloomed fabrics recognition with deep learning |
| title_short | Handloomed fabrics recognition with deep learning |
| title_sort | handloomed fabrics recognition with deep learning |
| topic | Textile loom type Handloom fabric Powerloom fabric Automated identification Artificial intelligence Deep learning |
| url | https://doi.org/10.1038/s41598-024-58750-z |
| work_keys_str_mv | AT lipibmahanta handloomedfabricsrecognitionwithdeeplearning AT devarajmahanta handloomedfabricsrecognitionwithdeeplearning AT taiburrahman handloomedfabricsrecognitionwithdeeplearning AT chandanchakraborty handloomedfabricsrecognitionwithdeeplearning |