Variable Data Printing Labels Market Report is Segmented by Component (Hardware, Software and Services), by Label (Release Liner Labels and Linerless Labels), by Printing Method (Thermal Transfer Printing, Direct Thermal Printing, Inkjet Printing, Electrophotography, Flexographic Printing and Other Methods), by End-Use Industry (Healthcare, Retail & E-Commerce, Food & Beverage, Logistics and Other End-Use Industries), and by Geography (North America, Europe, Asia Pacific, South America and Middle East and Africa). The Market Sizing and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

With the advent and expansion of social networking, the amount of generated text data has seen a sharp increase. In order to handle such a huge volume of text data, new and improved text mining techniques are a necessity. One of the characteristics of text data that makes text mining difficult, is multi-labelity. In order to build a robust and effective text classification method which is an integral part of text mining research, we must consider this property more closely. This kind of property is not unique to text data as it can be found in non-text (e.g., numeric) data as well. However, in text data, it is most prevalent. This property also puts the text classification problem in the domain of multi-label classification (MLC), where each instance is associated with a subset of class-labels instead of a single class, as in conventional classification. In this paper, we explore how the generation of pseudo labels (i.e., combinations of existing class labels) can help us in performing better text classification and under what kind of circumstances. During the classification, the high and sparse dimensionality of text data has also been considered. Although, here we are proposing and evaluating a text classification technique, our main focus is on the handling of the multi-labelity of text data while utilizing the correlation among multiple labels existing in the data set. Our text classification technique is called pseudo-LSC (pseudo-Label Based Subspace Clustering). It is a subspace clustering algorithm that considers the high and sparse dimensionality as well as the correlation among different class labels during the classification process to provide better performance than existing approaches. Results on three real world multi-label data sets provide us insight into how the multi-labelity is handled in our classification process and shows the effectiveness of our approach.

120 Global import shipment records of Chart Data Label with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This file contains the data elements used for searching the FDA Online Data Repository including proprietary name, active ingredients, marketing application number or regulatory citation, National Drug Code, and company name.

4576 Global export shipment records of Labels with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Summer-Project/mirror-data-label-1 dataset hosted on Hugging Face and contributed by the HF Datasets community

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Snapshot img

Labels Market Size 2024-2028

The labels market size is forecast to increase by USD 13.17 billion at a CAGR of 5.97% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing demand for sleeve labels in various industries, particularly food packaging. Sleeve labels, including shrink sleeves and stretch sleeves, have gained popularity due to their ability to provide full-coverage branding and product information. The implementation of barcode technology is another trend driving market growth, enabling efficient inventory management and supply chain optimization. However, the market faces challenges such as rising raw material prices, with key materials like polypropylene (PP), polyethylene (PE), polybutylene terephthalate (PBT), and PET experiencing price fluctuations. Manufacturers are responding by exploring alternative materials and production methods to mitigate these costs.
Overall, the market is expected to continue its growth trajectory, fueled by these trends and challenges.

What will be the Size of the Labels Market during the Forecast Period?

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The market encompasses a diverse range of materials, including glass, metals, wood, natural rubber, plastics, and various plastic resins such as polystyrene (PS), polyethylene (PE), polypropylene (PP), polybutylene terephthalate (PBT), polyphenylene oxide (PPO), polyurethane (PU), polyvinyl chloride (PVC), polyethylene terephthalate (PET), polycarbonate (PC), polysulfone (PSU), polyamide (PA), polyphenylsulfone (PPSU), and others. 
This market exhibits robust growth, driven by the increasing demand for labels in various sectors, particularly packaging. Plastics dominate the market due to their versatility, cost-effectiveness, and durability. Key trends include the growing preference for sustainable and eco-friendly labels, the adoption of digital printing technologies, and the increasing use of smart labels with RFID and NFC capabilities.
The market is expected to continue its expansion, driven by these trends and the ever-evolving needs of industries worldwide.

How is this Labels Industry segmented and which is the largest segment?

The labels industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Consumer goods
  Cosmetics and personal care
  Pharmaceuticals
  Others


Type

  Pressure-sensitive label
  Glue-applied label
  Sleeve label
  In-mold labels


Geography

  APAC

    China
    Japan


  Europe

    Germany
    France


  North America

    US


  South America



  Middle East and Africa

By End-user Insights

The consumer goods segment is estimated to witness significant growth during the forecast period. In the consumer goods sector, labels serve essential functions such as providing product information, differentiating offerings, and adhering to regulatory requirements. Labeling applications span across various product categories including food packaging, beverages, clothing, toiletries, and kitchenware. Companies In the Fast-Moving Consumer Goods (FMCG) industry frequently update labels to align with evolving consumer preferences. Regulatory bodies like China and Brazil impose specific labeling guidelines for various product types. In the packaging sector, plastics, such as polyethylene (PE), polyethylene terephthalate (PET), polyvinyl chloride (PVC), and polyamide (PA), dominate label production due to their versatility and cost-effectiveness. Building & construction and medical devices segments also utilize engineering plastics like Polycarbonate (PC), Polysulfone (PSU), Polypropylene (PP), and Polyurethane (PU) for labels. Labeling trends extend to sectors like electric vehicles (EVs) and the Clean Seas campaign, which promote eco-friendly alternatives to traditional plastics.

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The consumer goods segment was valued at USD 16.37 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 44% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The market in APAC is experiencing significant growth due to increasing demand for packaged food and beverages, driven by lifestyle and demographic changes In the middle-class population. This trend is particularly prominent in developing countries like India and China. Additionally, the rise in exports from ASEAN countries and the increasing demand for packaged personal care products are further fuel

United States Exports of labels of paper or paperboard, printed or not to San Marino was US$11.25 Thousand during 2012, according to the United Nations COMTRADE database on international trade. United States Exports of labels of paper or paperboard, printed or not to San Marino - data, historical chart and statistics - was last updated on March of 2025.

United States Imports of labels of paper or paperboard, printed or not from Zambia was US$2.6 Thousand during 2024, according to the United Nations COMTRADE database on international trade. United States Imports of labels of paper or paperboard, printed or not from Zambia - data, historical chart and statistics - was last updated on March of 2025.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet North America contains data across North America, which accounts for ~13% of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice.

There are a total of 1561 image chips of 256 x 256 pixels in LandCoverNet North America V1.0 spanning 40 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):
* Sentinel-1 ground range distance (GRD) with radiometric calibration and orthorectification at 10m spatial resolution
* Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution
* Landsat-8 surface reflectance product from Collection 2 Level-2

Radiant Earth Foundation designed and generated this dataset with a grant from Schmidt Futures with additional support from NASA ACCESS, Microsoft AI for Earth and in kind technology support from Sinergise.

Product Labels approved by TABC before Sept 1, 2021. Learn more about this data and how to search it at https://www.tabc.texas.gov/public-information/approved-labels-search/.

Guyana Exports of labels of paper or paperboard, printed or not to Trinidad And Tobago was US$3.6 Thousand during 2024, according to the United Nations COMTRADE database on international trade. Guyana Exports of labels of paper or paperboard, printed or not to Trinidad And Tobago - data, historical chart and statistics - was last updated on March of 2025.

The size and share of the market is categorized based on Type (Paper Labels, Film Labels, Linerless Labels, Thermal Transfer Labels, Direct Thermal Labels) and Application (Food & Beverage, Pharmaceuticals, Personal Care, Retail, Logistics, Automotive) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet Asia contains data across Asia, which accounts for ~31% of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice.
There are a total of 2753 image chips of 256 x 256 pixels in LandCoverNet South America V1.0 spanning 92 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):
* Sentinel-1 ground range distance (GRD) with radiometric calibration and orthorectification at 10m spatial resolution
* Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution
* Landsat-8 surface reflectance product from Collection 2 Level-2

Radiant Earth Foundation designed and generated this dataset with a grant from Schmidt Futures with additional support from NASA ACCESS, Microsoft AI for Earth and in kind technology support from Sinergise.

This dataset was created by the DC Office of Planning and provides a simplified representation of the neighborhoods of the District of Columbia. These boundaries are used by the Office of Planning to determine appropriate locations for placement of neighborhood names on maps. They do not reflect detailed boundary information, do not necessarily include all commonly-used neighborhood designations, do not match planimetric centerlines, and do not necessarily match Neighborhood Cluster boundaries. There is no formal set of standards that describes which neighborhoods are represented or where boundaries are placed. These informal boundaries are not appropriate for display, calculation, or reporting. Their only appropriate use is to guide the placement of text labels for DC's neighborhoods. This is an informal product used for internal mapping purposes only. It should be considered draft, will be subject to change on an irregular basis, and is not intended for publication.

1369 Global import shipment records of Sticker Labels with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Chile Imports of labels of paper or paperboard, printed or not from Trinidad And Tobago was US$15 during 2023, according to the United Nations COMTRADE database on international trade. Chile Imports of labels of paper or paperboard, printed or not from Trinidad And Tobago - data, historical chart and statistics - was last updated on March of 2025.

Bottle Labels

## Overview

Bottle Labels is a dataset for object detection tasks - it contains Labels annotations for 644 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).