April 24, 2023
The fashion industry has been undergoing a significant transformation over the years, with pre-loved fashion gradually gaining popularity.
Recent statistics suggest that this industry is going to continue growing at an impressive rate.
By 2027, the global secondhand clothing market is projected to reach $350 billion. More than half of all shoppers are now actively looking for secondhand apparel, and over a third of them are even prioritizing these purchases over brand-new ones.
So why is this happening? Well, it turns out that inflation is one of the major reasons why shoppers are turning to pre-loved fashion.
But with this rapid growth comes some challenges, particularly in how we structure data from fashion item labels. That's where Label Recognition comes in!
In this blog post, we're going to dive deeper into this feature and explore how it's making a difference in the pre-loved fashion industry.
So read on and get ready for some insights!
Label recognition is an innovative feature tailored for the pre-loved fashion industry.
Powered by state-of-the-art AI algorithms, this advanced solution accurately and efficiently analyzes visual content to automatically extract relevant information from fashion item labels.
By eliminating the need for manual data input, it saves time and streamlines the process for sellers and buyers.
With its cutting-edge features, label recognition unlocks the full potential of pre-owned fashion items, providing significant advantages to the industry.
In summary, this must-have tool offers an efficient and effective solution for structuring data from fashion item labels using cutting-edge AI technology, benefiting both sellers and buyers alike.
The process of label information extraction is accomplished through a versatile and powerful solution.
It accepts multiple product images as input, identifies the presence of labels, and accurately extracts relevant information from them.
This includes details such as the brand, size, material composition, and manufacturing location of the fashion item, returned in a structured data format.
Accessible through an API, it can be seamlessly integrated into any client's tech stack with ease.
The AI model behind the Label Recognition feature comprises two components: the classifier and the AI transformer end-to-end model.
The classifier is an AI model that determines whether a label is present in a product image, while the AI transformer end-to-end model is responsible for detecting and extracting structured data from the text on the fashion label.
The label recognition feature brings numerous advantages the pre-loved fashion industry, specifically to second-hand marketplaces:
If you operate a pre-loved fashion business, you are likely familiar with the time-consuming and inefficient manual data entry process.
According to a survey, over 40% of employees report spending a significant portion of their work week on repetitive tasks like data collection and data entry, with a particular emphasis on second-hand marketplaces that acquire fashion items from consumers. These marketplaces often face challenges with incomplete data fields, making it difficult for shoppers to find what they want to buy.
Fortunately, the Label Recognition feature offers a solution to streamline this process and make it more efficient and accurate, specifically for second-hand marketplaces.
This innovative feature allows marketplaces to easily handle large volumes of product images containing labels, enhancing productivity and reducing the time spent on manual data entry.
By accurately extracting data from fashion labels, marketplaces can optimize their data entry workflows, ensuring precise and reliable product information for an improved user experience.
If you run a second-hand marketplace, this feature can significantly improve the search and product discovery experience for shoppers.
Automatically extracting important data from fashion item labels, such as brand, size, material, and origin, enhances the accuracy and completeness of product data, making it easier for shoppers to find what they are looking for.
Furthermore, this feature can also enhance the listing experience for sellers by pre-populating the data fields with automatically recognized data. This eliminates the need for sellers to manually enter data fields such as brand, size, and material, making it faster and easier for them to create listings, leading to increased seller satisfaction and engagement.
In summary, the Label Recognition feature is a game-changer for anyone in the pre-loved fashion industry, particularly for second-hand marketplaces. It streamlines the data entry process, improves user experience, and ultimately contributes to a more sustainable fashion industry.
Say goodbye to cumbersome manual data entry and embrace the Label Recognition feature for a more streamlined and productive workflow in the pre-loved fashion industry.
In conclusion, the Label Recognition feature is a valuable tool for the rapidly growing pre-loved fashion industry.
With advanced AI algorithms that automatically extract key information from fashion item labels, this feature simplifies data organization and brings numerous benefits.
By improving data input efficiency, enhancing user experience, increasing productivity, and reducing inaccuracies in product listings, the feature streamlines operations in the pre-loved fashion industry.
Furthermore, it promotes item reuse and contributes to a more sustainable fashion industry by reducing environmental impact.
Seamlessly integrating into any client's tech stack, this feature is a must-have for anyone looking to optimize their pre-loved fashion items.
To get started, book a demo with one of our expert teams to see our product in action.
Once you've had the demo, you can implement it for free for 3 months.
Take advantage of this innovative solution and revolutionize your pre-loved fashion business with Label Recognition.