Revolutionizing Saree Shopping: How AI is Making Saree Texture Identification a Breeze

For centuries, sarees have been an integral part of Indian women's wardrobes. These elegant garments come in a dazzling array of materials, each with its own unique texture and feel. Traditionally, selecting the perfect saree involves touching and feeling the fabric, a process that is not always feasible with the rise of online shopping. However, modern technology is stepping in to bridge this gap.

The Challenge of Choosing the Perfect Saree Online

One of the biggest challenges of buying sarees online is the inability to touch the fabric. With so many different materials available, from silk to cotton to chiffon, it's tough to know exactly what texture you're getting just by looking at a picture. This issue is compounded for those who might not have extensive experience with the various textures and types of saree materials.

Enter Deep Learning: A Technological Solution

Thanks to rapid advancements in smartphone technology and artificial intelligence (AI), we're now able to tackle this problem in innovative ways. Imagine being able to look at a photo of a saree and instantly know what texture it is made of. This is no longer science fiction but a reality made possible through deep learning.

How Does It Work?

A groundbreaking framework has been developed that uses deep learning to classify saree textures quickly and accurately. Here’s how it works:

1. Image Capture: Using your smartphone, you take a picture of the saree.

2. Mask RCNN: This advanced AI tool helps to segment and identify the saree in the image by generating patches that focus on the fabric's texture.

3. VGG-16 Network: This is where the magic happens. The VGG-16 network, a type of deep learning model, analyzes these patches to classify the saree texture with impressive accuracy.

Why Mask RCNN and VGG-16?

Mask RCNN is a state-of-the-art model for image segmentation. It ensures that the saree is accurately detected in the image, isolating the fabric from the background. Once we have these precise patches, the VGG-16 network comes into play. This model is known for its robust performance in image recognition tasks. It processes the texture details in the patches and determines the saree material.

Exceptional Accuracy

In a paper by D. S. Dakshina, Dr. P. Jayapriya and R. Kala (source),  deep learning pipeline outperforms existing methods, achieving an impressive 97.41% accuracy in saree material classification. This means you can shop for sarees online with greater confidence, knowing that the fabric texture identified by the AI is almost always spot on.

The Future of Saree Shopping

With this technology, the traditional tactile experience of saree shopping can be brought to the digital world. You’ll be able to make informed decisions about saree materials without having to physically touch them. This innovation is set to revolutionize how we shop for sarees, making it easier, faster, and more reliable.

The integration of deep learning into saree shopping is a game-changer. By harnessing the power of Mask RCNN and the VGG-16 network, a system is developed that accurately identifies saree materials from images. This not only enhances the online shopping experience but also ensures that you get exactly what you expect. Say goodbye to the guesswork and hello to smarter saree shopping!

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Handloom Weaving: Taking a Toll on the Joints !!!

In the ancient city of Varanasi, where tradition weaves its way through the fabric of daily life, handloom weaving stands as a testament to centuries-old craftsmanship. Yet, amid the intricate patterns and vibrant colors, a silent struggle unfolds—one that echoes through the aches and pains of the artisans themselves.

Picture this: hours spent hunched over, shoulders tense, back curved, as skilled hands move rhythmically across the loom. It's a scene of dedication and artistry, but also one fraught with risk. Poor posture, exacerbated by the demands of their craft, takes its toll on the bodies of handloom weavers, leading to a myriad of musculoskeletal problems.

A recent study done by Sunita Dixit, which is published titled “Anthropometric Measurement & Assessment of Occupational Ergonomic Risks of Handloom Weaving in Varanasi District” delves into this issue, shedding light on the physical challenges faced by these artisans. Through careful evaluation of anthropometric measurements and body mass index, researchers aimed to assess the physical fitness of handloom weavers. What they uncovered was illuminating—a high prevalence of musculoskeletal disorders, stemming from the prolonged hours of static work and awkward postures inherent in traditional handloom designs.

As reported by her “In traditional old looms, normally there is no workstation adjustability and adjustment of weaving height is difficult that causes the awkward postures of the upper body. Inappropriately designed hand tools and the kind of the task are the chief causes of awkward postures of wrists and fingers. “

                        

As can be seen from the results a full 86% of the weavers surveyed have to work with the  postures which are in the top risk category. 

The findings underscore a pressing need for intervention. By understanding the ergonomic demands of handloom weaving and the strain it places on the body, we can pave the way for meaningful change. From redesigning traditional looms to accommodate healthier working postures to implementing targeted interventions aimed at mitigating musculoskeletal risks, there are actionable steps we can take to support the well-being of handloom weavers.

One crucial tool in this endeavor is the Rapid Entire Body Assessment (REBA), which offers a systematic approach to evaluating working postures and identifying areas for improvement. Through observations of handloom weavers in action, researchers assigned scores to various body parts, pinpointing areas of concern and highlighting opportunities for intervention.

At the heart of this research lies a simple yet profound question: Are handloom weavers suffering because of unnatural postures? The answer, it seems, is a resounding yes. But with awareness comes opportunity—the opportunity to advocate for change in the ergonomic design of the machines and other adjustment , to champion the well-being of artisans whose craft is not only a livelihood but a cultural heritage.

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Is Tussar Silk Inferior to Mulberry Silk ?

In a paper entitled  "Study of property and structural variants of mulberry and Tussar silk filaments" by professor Mohan Gulrajani, one can get several hints which may lead to the answer to the question.

"A glance at the typical tensile behaviour reveals that the stress-strain curve of these two varieties is distinctly different, in that tasar shows a clear yield point and very high elongation compared to the mulberry filament."

Conclusion 1:  Tussar silk can undergo significant stretching before permanently deforming.

The tusar silk stress-strain curve exhibits a clear yield point. A yield point is a point on the stress-strain curve where the material transitions from elastic deformation (where it returns to its original shape after the force is removed) to plastic deformation (where it retains some deformation even after the force is removed). This suggests that Tussar silk can undergo significant stretching before permanently deforming. 

Conclusion 2:  Tussar can stretch a lot before reaching its breaking point compared to mulberry silk.

The stress-strain curve of tussar silk also shows very high elongation compared to mulberry silk. Elongation refers to how much a material stretches before breaking. The fact that tussar silk exhibits high elongation means it can stretch a lot before reaching its breaking point compared to mulberry silk.

In contrast, mulberry silk does not show as pronounced a yield point and has lower elongation compared to tussar silk. This implies that mulberry silk is less flexible and may have a more limited ability to stretch before breaking compared to tasar silk.

Why there is a difference in their properties

One answer can  be density.  The density of mulberry is higher ( 1.35 g/cc) as compared to tussar ( 1.30 g/cc). This suggests a relatively poor degree of orientation and less order in Tussar, which gives to lower modulus and elongation behavior of tussar.

These values have their commercial and functional implications. 

Can Silk be Machine Washed

At least a study suggests so. 

A paper titled "Study of property and structural variants of mulberry and Tussar silk filaments" by professor Mohan Gulrajani has suggested this idea. 

Earlier research suggested that the wet strength of silk specially Mulberry reduces considerably when subjected to water during laundering. This happens because in an aqueous environment, the hydrogen bonds between the molecules break. These bonds are crucial for maintaining the structure and strength of the fibers.

However the paper suggests that " silks can be machine washed at 40-60ºC provided one uses appropriate washing procedures, such as the use of neutral detergents".

The results for both Tussar and Mulberry found that " the tenacity and elongation at break are not

significantly different in dry or wet state ". However there is slight decrease in modulus. The figure given below talks about the result. 

A reduction in modulus would make the fiber less stiff.

Modulus, specifically in the context of materials science, refers to the measure of a material's stiffness or rigidity. It indicates the ability of a material to resist deformation under an applied force. Modulus is typically expressed in terms of stress divided by strain, where stress is the force applied per unit area, and strain is the resulting deformation.

When the modulus of a material decreases, it means that the material becomes less resistant to deformation for a given stress. In other words, it becomes more flexible or less stiff. Conversely, an increase in modulus would indicate that the material becomes stiffer or more resistant to deformation.

Then why it is not advised not to launder Pure silk sarees ?

The answer lies in the properties of commercially available silk fabrics or sarees. The above study was done after fully degumming the yarn. However, in commercially available silk fabric, the yarn is not fully degummed, there is always a residual gum or sericin. In the study about 20% sericin was found in mulberry and 5% in tussar.  On wetting, the sericin weakens, and allows inter filament slippage, which in turn leads to a drastic reduction in mechanical properties. Hence the strength of the wet silk gets reduced. 

What is sericin, what is silk fiber composed of ?

Silk fiber is primarily composed of two main proteins: fibroin and sericin. These proteins are produced by specialized glands in the silk-producing organisms, such as silkworms (Bombyx mori). The composition of silk fiber can vary depending on factors such as the species of the silk-producing organism and the conditions under which the silk is produced.

Fibroin: Fibroin is the structural protein that forms the core of silk fibers. It constitutes the majority of the silk fiber's mass and is responsible for its strength and resilience. Fibroin is a fibrous protein composed mainly of amino acids such as glycine, alanine, and serine. The exact composition and arrangement of amino acids within fibroin contribute to its unique mechanical properties, including its tensile strength and elasticity.

Sericin: Sericin is a glue-like protein that surrounds and binds the fibroin filaments together within the silk cocoon. It serves to protect the fibroin and provide cohesion to the silk fiber structure. Sericin is composed of various proteins and amino acids, with its composition varying depending on factors such as the silk-producing species. Sericin is typically removed from silk fibers during processing to improve their texture and appearance, leaving behind only the fibroin core.

In addition to proteins, silk fiber may also contain small amounts of other substances such as lipids, sugars, and minerals. These minor components can influence the properties of silk fibers but are present in much smaller quantities compared to fibroin and sericin.

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