Behind the Sarees: The Physical Toll of Being a Saree Seller

In the bustling world of textile retail, particularly in saree-selling shops in Ahmedabad, workers face unique occupational challenges that often go unnoticed. A recent study sheds light on the musculoskeletal disorders (MSDs) affecting saree sellers due to their long hours, repetitive movements, and awkward postures. Let's explore the findings of this insightful study and the potential interventions that could improve the working conditions of these workers.

The study, conducted on 56 saree sellers in Ahmedabad, reveals startling data about the physical toll this occupation takes. Nearly all participants (99%) worked eight hours a day, with a significant portion (70%) working seven days a week. These long hours, combined with repetitive movements and awkward postures, contribute to a high prevalence of musculoskeletal disorders, particularly in the lower limbs.

More than half of the workers (54%) reported experiencing pain in the past 12 months, with the most common issues occurring in the knees (17%) and ankles (7%). The repetitive action of getting up and sitting down, often more than five times daily, exacerbates these conditions. Workers reported comfort in positions such as cross-legged sitting or kneeling on the floor, but these postures can further strain the body over time.

Despite the high prevalence of MSDs, awareness and utilization of treatment options remain low. While 62% of the affected workers underwent surgical treatment, only a tiny percentage (7%) received physiotherapy. This suggests a need for increased awareness of non-invasive treatments like physiotherapy, which could significantly alleviate discomfort and prevent further complications.

The study emphasizes the need for ergonomic interventions in saree-selling shops. Adjusting workspaces to reduce awkward postures and incorporating regular breaks to minimize repetitive movements could go a long way in preventing musculoskeletal disorders. Implementing proper seating arrangements, ensuring that workers do not have to sit or kneel for extended periods, and educating them on proper posture and movement techniques could greatly improve their quality of life.

Saree sellers, like many workers in physically demanding jobs, are vulnerable to long-term health issues caused by poor ergonomics and strenuous working conditions. The findings of this study highlight the urgent need for ergonomic solutions and greater awareness of physiotherapy in this industry. By prioritizing the health of saree sellers, we can help reduce the incidence of musculoskeletal disorders and improve the well-being of these essential workers.

The full study, published in the International Journal for Multidisciplinary Research, serves as a wake-up call for better workplace practices in the textile industry, particularly for saree sellers who endure long hours and repetitive movements daily. Let’s strive to make their workplaces healthier and more supportive.

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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[1,2,3,4]. These elegant garments come in a dazzling array of materials, each with its own unique texture and feel[5,6,7,8]. Traditionally, selecting the perfect saree involves touching and feeling the fabric, which is not always feasible with the rise of online shopping[5,9,10,11]. 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.[12,13]

Enter Deep Learning: A Technological Solution

Thanks to rapid advancements in smartphone technology and artificial intelligence (AI), we can tackle this problem innovatively. 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: You take a picture of the saree using your smartphone.

2. Mask RCNN: This advanced AI tool helps segment and identify the saree in the image by generating patches focusing 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 accurately classify the saree texture.

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.[14] 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.[15,16,17,18]

Exceptional Accuracy

In a paper by D. S. Dakshina, Dr. P. Jayapriya, and R. Kala (source), the 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 can make informed decisions about saree materials without physically touching 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 enhances the online shopping experience and ensures that you get exactly what you expect. Say goodbye to the guesswork and hello to smarter saree shopping!

Bibliography:

1. Bhatnagar, Parul. Traditional Indian Textiles. Abhishek Publications, 2005.

2. Franck, Irene M., and David M. Brownstone. The Silk Road: A History. Facts on File, 1986.

3. Gordon, Beverly. Textiles: The Whole Story: Uses, Meanings, Significance. Thames & Hudson, 2011

4. Dehejia, Vidya. Indian Art. Phaidon Press, 1997.

5. Chishti, Rta Kapur, and Amba Sanyal. Saris: Tradition and Beyond. Roli Books, 2010.

6. Gillow, John, and Nicholas Barnard. Traditional Indian Textiles. Thames & Hudson, 2008.

7. Murphy, Veronica. The Indian Textile Sourcebook. A&C Black, 2011.

8. Naik, Shailaja D. Traditional Embroideries of India. APH Publishing, 1996.

9.    Barnes, Ruth, and Joanne B. Eicher. Dress and Gender: Making and Meaning in Cultural Contexts. Bloomsbury Publishing, 1997.

10. Pal, Pratapaditya. Indian Saris: Traditions - Perspectives - Design. Mapin Publishing, 2006.

11. Banerjee, Mukulika, and Daniel Miller. The Sari. Berg Publishers, 2008.

12. Sengupta, Joy. "E-commerce and Indian Fashion: How Digital Platforms Are Changing the Way We Shop." The Indian Journal of Business, vol. 22, no. 4, 2019, pp. 45-67.

13. Rathi, Meenal. "Challenges of Online Shopping for Traditional Wear in India." International Journal of Marketing & Technology, vol. 7, no. 5, 2018, pp. 115-128.

14. He, Kaiming, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick. "Mask R-CNN." Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2961-2969.

15. Simonyan, Karen, and Andrew Zisserman. "Very Deep Convolutional Networks for Large-Scale Image Recognition." arXiv preprint arXiv:1409.1556, 2014.

16. Russakovsky, Olga, et al. "ImageNet Large Scale Visual Recognition Challenge." International Journal of Computer Vision, vol. 115, no. 3, 2015, pp. 211-252.

17. Simonyan, Karen, and Andrew Zisserman. "Deep Convolutional Networks for Large-Scale Image Recognition." International Conference on Learning Representations (ICLR), 2015.

18. Yosinski, Jason, Jeff Clune, Yoshua Bengio, and Hod Lipson. "How Transferable Are Features in Deep Neural Networks?" Advances in Neural Information Processing Systems (NIPS), 2014.

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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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Some Notes about Arani Sarees

 Source

1. Until 1995, only small motifs were created using 'Adai' or dobbies. Now bigger motifs with Jacquards are also in vogue.

2. Arani is located in the Tiruvannamalai district of Tamil Nadu.

3. In Tamil, Aru means river and Ani means adorning. Arani means a place made beautiful by rivers.

4. In Arani, still street sizing is practiced

5. These sarees are characterized by Korvai and Thazhampoo Rekku on the borders. 

In Hindi, "Thazhambu flower" is known as "केवड़ा फूल" (Kewda Phool). Kewda is a type of fragrant flower commonly used in perfumes, culinary preparations, and religious rituals in India. It is also known as Pandanus flower in English.

In the context of sarees, "ரேக்கு" (rekku) typically refers to the decorative borders or edges of the saree. These borders are often woven or embroidered onto the saree fabric and can vary in width and design. The term "rekku" is used to describe these intricate patterns or embellishments that adorn the edges of the saree, enhancing its beauty and elegance.

Thazhambu Flower

Thazhampoo Rekku

6. Both Frame looms and pit looms are used to weave the sarees. 

7. Arani weavers are mostly composed of Saurashtrians from Gujarat who came during the Vijayanagara Period. 

8. Arni Dobby sarees are lightweight and made with single color yarn using a fly shuttle. 

9. This region also produces Kumbakonam korvai Sarees

Kumbakonam Sarees

10. Arani Kottadi ( Checked pattern is very Popular)

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Automatic Fabric Defect Detection: New Approaches

The blog post is based on the this article:

In the ever-evolving landscape of textile manufacturing, maintaining the highest fabric quality is paramount. Traditionally, defect inspection has relied on human visual scrutiny, often employing semi-automated methods. However, this approach is labor-intensive and costly, prompting the need for more efficient and cost-effective solutions. Enter automatic inspection systems for defect detection, leveraging cutting-edge technologies like artificial neural networks, threshold segmentation, structural, statistical, and model-based approaches, as well as computer vision methods. This article explores the various methods employed in automatic fabric defect detection and their impact on revolutionizing fabric quality control.

The Need for Automation in Fabric Inspection

Fabric defects can range from irregularities in the weave to discolorations and tears. Detecting these imperfections manually is not only time-consuming but also prone to human error. Automatic fabric inspection systems aim to streamline this process, offering a more efficient and reliable solution. The primary goals include reducing time and cost wastage associated with defects, ensuring consistent quality, and meeting the ever-growing demands of the textile industry.

Methodologies in Automatic Defect Detection

1. Artificial Neural Networks (ANNs): Artificial Neural Networks have gained prominence in various fields, including fabric defect detection. ANNs mimic the human brain's structure, allowing them to learn and adapt to patterns. In fabric inspection, ANNs analyze large datasets of fabric images to identify and classify defects. The advantage lies in their ability to recognize complex patterns, making them effective in distinguishing subtle fabric irregularities.

2. Threshold Segmentation: Threshold segmentation involves setting a threshold value to distinguish between defective and non-defective areas of the fabric. This method relies on pixel intensity, where variations beyond a certain threshold are classified as defects. While threshold segmentation is simpler compared to neural networks, it proves effective in detecting visible defects and is computationally less intensive.

3. Structural and Statistical Approaches: Structural and statistical methods involve analyzing the fabric's structural features and statistical properties to identify defects. This may include analyzing the texture, thread density, and overall fabric composition. These methods offer a robust solution for defect detection, especially when combined with other approaches, providing a more comprehensive inspection.

4. Model-Based Approaches: Model-based approaches involve creating mathematical models of defect-free fabric, enabling the system to detect deviations from the established norm. This method is highly adaptable and effective in identifying both subtle and prominent defects. However, it requires precise modeling and may be more computationally demanding.

5. Computer Vision with Multi-Layer Neural Networks: Integrating computer vision with multi-layer neural networks represents a sophisticated approach to fabric defect detection. This method combines the strengths of computer vision for image processing and neural networks for pattern recognition. The result is a powerful system capable of accurately identifying and classifying various defects with high precision.

Empirical Outcomes and Benefits

Empirical evidence suggests that visualized approaches to fabric defect detection offer several key benefits. These include:

1. High Analyzing Speed: Automatic fabric inspection systems exhibit remarkable speed in analyzing fabric for defects. This accelerated pace enhances production efficiency and allows manufacturers to meet tight deadlines without compromising on quality.

2. Easy Utilization: The user-friendly nature of these systems ensures easy integration into existing manufacturing processes. Minimal training is required for operators to navigate and manage the automatic inspection systems effectively.

3. Noise Immunity: Automatic defect detection systems are less susceptible to noise and external factors that may affect manual inspections. This ensures a more reliable and consistent evaluation of fabric quality, leading to a reduction in false positives and negatives.

4. Meeting Requirements for Automatic Fabric Defect Inspection: Automatic fabric inspection systems effectively meet the stringent requirements of the textile industry. The combination of accuracy, speed, and ease of use positions these systems as essential tools for ensuring high-quality fabric production.

In conclusion, the integration of automatic fabric inspection systems represents a significant leap forward in fabric quality control. The diverse methodologies, ranging from artificial neural networks to model-based approaches, showcase the versatility of these systems in identifying defects with precision and efficiency. The empirical outcomes highlight the benefits of adopting such technology, including increased analyzing speed, ease of utilization, noise immunity, and meeting the industry's stringent requirements. As the textile industry continues to evolve, embracing these innovative solutions will undoubtedly play a pivotal role in enhancing overall fabric quality and production efficiency.

Case Studies

1. This study utilizes Fast Fourier Transform and Cross-correlation techniques for spatial domain analysis, followed by a thresholding operation to enhance defect detection accuracy. The approach is validated through simulations on plain fabric, optimizing parameters and considering noise. The proposed vision-based fabric inspection prototype aims for on-loom implementation, ensuring 100% coverage during fabric construction.

2. In this implementation to facilitate accurate inspection, a specialized LED system is employed to illuminate the fabric consistently and evenly. This lighting setup enhances visibility and aids in the precise detection of defects. Additionally, the system incorporates an encoder to measure fabric movement, ensuring synchronized data analysis.

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Kanchipuram Sarees: A case in Challenges in GI Certification

This thought-provoking article points out the fact that how GI certification is not complete, neither it does justice to the realities of production. Kanchipuram Sarees is taken as an example. I could take away two points from the study.

Point 1: Rhetoric of Authenticity 

The author talks about GI as a "rhetoric of authenticity influences how artisanal products are valued and marketed " as practiced in Europe.

Authenticity as a Value Proposition: In many markets, the perceived authenticity of a product can significantly enhance its value. Consumers often associate authenticity with quality, tradition, and the preservation of cultural heritage. This is particularly true for artisanal products, where the history, origin, and traditional methods of creation play a crucial role in defining the product's identity and appeal.

Marketing and Perception: Products marketed as "authentic" can attract a premium in the marketplace. This is because consumers are willing to pay more for items that are seen as genuine representations of a culture or tradition. The marketing of products often emphasizes their authenticity to tap into this consumer sentiment, highlighting traditional manufacturing processes, materials, and the cultural significance of the product.

Impact on Artisanal Products: For artisanal products like the Kanchipuram and Arani saris mentioned previously, authenticity becomes a key selling point. Kanchipuram saris, known for their quality and traditional designs, are seen as the gold standard. Saris that do not meet these traditional standards but are marketed under the same name occupy a different niche, appealing to consumers looking for something that appears traditional and authentic but is perhaps more affordable.

Challenges of Authenticity: The emphasis on authenticity also presents challenges. It can lead to strict categorizations of what is considered "authentic," potentially excluding products that innovate or diverge from traditional methods. Additionally, the demand for authentic products can lead to exploitation, where items are marketed as authentic without truly adhering to the traditional criteria, diluting the very concept of authenticity.

Cultural and Economic Implications: The rhetoric of authenticity affects not just the economic value of products but also cultural perceptions. It can elevate certain traditions and crafts to a status that commands respect and preservation, but it can also reinforce rigid definitions of culture that may exclude evolving practices.

Point 2: GI as a standard is destabilized in a production scenario

The Issue of Duplicates and Quality Variation: In the scenario you describe, the production of saris that are marketed under the umbrella of a GI-tagged product (like the Kanchipuram sari) includes versions that do not necessarily meet the high standards or specific criteria that the GI designation is supposed to guarantee. This situation arises when artisans produce variations of the sari that cater to different market segments, often altering the quality to meet different price points.

De-stabilization of the GI Standard: The introduction of such "duplicates" or varied quality versions of the GI-tagged product challenges the integrity of the GI standard. Since the GI tag is meant to assure consumers of a certain level of quality and authenticity tied to a geographical region, the presence of lower-quality versions under the same name can dilute the value of the GI tag and potentially mislead consumers.

Artisan Choice and Market Segmentation: Artisans face a choice between upholding the high standards associated with their GI-tagged products and adapting their practices to produce lower-cost versions for broader market segments. This choice reflects the economic realities and pressures of the market, where there is demand for products at various price points, not just the premium segment that seeks authentic, high-quality artisanal goods.

Implications for GI Policy and Enforcement: Your argument suggests a need for stricter enforcement of GI standards and possibly a reevaluation of how these standards accommodate or discourage variations in quality. It raises questions about the role of GI tags in protecting the reputation of traditional crafts and the livelihoods of artisans while also addressing consumer demand for affordable products.

Balancing Authenticity, Quality, and Accessibility: Ultimately, the challenge lies in balancing the preservation of traditional methods and quality associated with GIs with the need to make these products accessible to a wider audience. This balance requires careful policy considerations, education of consumers about what GI tags represent, and perhaps the introduction of tiered classifications within a GI to acknowledge different quality levels without compromising the integrity of the original GI product.

As quoted by Author:

A Case about Real Zari

"The fact that Kanchipuram is fast transforming from a silk weaving town into a retail hub is testimony to the rising demand for the “duplicate” Kanchipuram sari that is indifferent to or eludes the GI’s precise specifications. The phenomenon is an example of what Herzfeld (2005) calls “cultural intimacy” where rules are flouted with impunity. The office originally handling GI applications and enforcements in Kanchipuram is now non-existent and the fact that the zari testing machine is not accurate or has been re-calibrated to show only the desired and/or acceptable reading is common knowledge among both the producers as well as those in positions of authority."

A case about Korvai Technique

The injunction to employ the korvai or three-shuttle weave for solid borders in the GI has further exacerbated compliance. The korvai technique requires an apprentice weaver to assist in throwing the third shuttle. Often this apprentice is a younger member of the weaver’s own family contributing to the work in the process of acquiring the skill of silk weaving at an early age. The enforcement of the Child

Labor (Prohibition and Regulation) Act from the 2000s in Kanchipuram ensures that hiring young apprentices be forbidden by law. New entrants to silk weaving, usually those who have woven in cotton, are either not deft enough to assist in three-shuttle silk weaving or demand much higher wages (equivalent to those of a highly skilled weaver) for a supplementary task. Considered to be laborious, time consuming and not worth the effort, korvai weaving is therefore a difficult and costly proposition for many local producers in Kanchipuram.  Many producers have made representations to the government to replace the korvai obligation in the GI with newer, more popular, weaving techniques like the jangla, or patterned weave.

Source

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Roadside Dyeing in India: How harmful is it for the Indian Dyers

This post is based on this Article

In a world where vivid colors weave into the very fabric of our lives, the unsung heroes behind these hues often face unseen challenges. Roadside dyers, integral to the textile industry, work tirelessly to bring color to our world. However, their occupation exposes them to serious health risks, a topic often overlooked but crucial in understanding the industry's human cost.

The Hidden Dangers of Chemical Dyes: Chemical dyes, known for their strong covalent bonds to textiles, are widely used for their durability and vibrant colors. Yet, these very attributes pose a significant health hazard to the dyers. Prolonged exposure to allergenic substances and irritant vapors from these dyes can lead to various health issues, from respiratory problems to skin allergies.

The Lack of Awareness and Safety Standards: The study reveals a concerning lack of awareness among dyers regarding the potential health risks associated with their profession. This casual attitude, coupled with inadequate occupational safety measures, puts them at an increased risk of health hazards. It's a wake-up call to the industry and authorities to prioritize the health and safety of these workers.

The Need for Immediate Action: The growing number of individuals in this unorganized sector highlights the urgent need for improved occupational safety and health standards. It's not just about providing protective gear or safer work environments; it's about educating the dyers on the risks and safe handling of these chemicals.

A Call for Change: This blog is a call to action - for industry leaders, policymakers, and consumers alike. As we embrace the beauty brought into our lives by these colors, let's not forget the hands that dye them. It's time to ensure that those hands are safe, healthy, and valued.

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