Data Extraction from Shipping Labels

Error-Free Data Extraction from Shipping Labels [For Logistics]

In the logistics industry, manually extracting data is a source of errors, delays, and operational inefficiencies. Data extraction from shipping labels is the process of converting unstructured information from sources like shipping labels into organized, usable data. This can be automated or performed manually.

Manual extraction is labor-intensive and error-prone, whereas automated methods are more efficient as they use technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Optical Character Recognition (OCR).

Automated data extraction significantly improves logistics operations by minimizing errors, accelerating data processing, and providing businesses with real-time insights into their supply chains. 

This blog will explore the technologies behind data extraction, its vital role in logistics, and the key features of KlearStack’s data extraction software. We’ll also provide a step-by-step guide on how to extract data from shipping labels with KlearStack.

What is OCR Technology and How Does it Work?

Optical Character Recognition, commonly known as OCR, is a technology that converts images of typed, handwritten, or printed text into machine-readable formats. It acts like a digital translator, deciphering the visual symbols on a document and rendering them into text that software applications can understand and process.  

In the context of shipping label data extraction, OCR is important. Shipping Labels OCR analyzes the images of shipping labels and identifies the characters representing recipient names, addresses, tracking numbers, and other important details. It then converts them into editable text. This extracted data is seamlessly integrated into logistics management systems, inventory databases, and other applications, automating processes that would otherwise be manual and time-consuming.  

OCR is the technological backbone of efficient data extraction from shipping labels. It enables businesses to streamline their logistics operations, improve data accuracy, and make informed decisions based on real-time insights.

Importance of Efficient Data Extraction in Shipping Labels

Manually extracting information from shipping labels is a tedious, error-prone task that significantly impedes logistics operations. Efficient data extraction from shipping labels is not just a convenience—it’s a necessity for modern logistics operations. The adoption of document digitization in the supply chain, including the use of shipping label scanners to efficiently scan shipping labels, can significantly alleviate these challenges. Automated data capture helps businesses in the following ways: 

• Reduce Costs: Manual data entry is labor-intensive and expensive. Automating extraction with tools like KlearStack frees up valuable resources and lowers operational expenses.
• Improve Accuracy and Speed: Automated models process data faster and more accurately than humans. Automated extraction eliminates typos and misinterpretations, leading to smoother operations and fewer costly mistakes.
• Prevent Errors: Minimizing human intervention with automated extraction significantly reduces the risk of errors, preventing issues such as misdirected shipments and incorrect billing.
• Detect Fraud: Data extraction tools can identify inconsistencies or unusual patterns in shipping data, helping to detect potential fraud or unauthorized activity.
• Enhance Operational Efficiency: Quick and accurate data access speeds up order processing, shipment tracking, and customer responses, improving overall operational efficiency.
• Boost Customer Satisfaction: Efficient data extraction in logistics translates to timely deliveries, accurate tracking information, and prompt responses to customer inquiries, contributing to a positive customer experience.
• Optimize Resource Allocation: By understanding shipping patterns, volumes, and costs, businesses can make informed decisions to optimize resource allocation and improve profitability.

Key Data Fields in Shipping Labels for Data Extraction 

Shipping labels contain a wealth of information important for logistics operations. Let’s explore the key data fields in shipping labels for data extraction and their significance:

1. Shipping Company: The carrier responsible for delivering the package. This information is essential for tracking shipments and resolving any delivery issues.

2. Origin/Return Address: The address from which the package is being shipped or where it should be returned if necessary. This data is used to manage inventory and handle returns.

3. Destination Address: The recipient’s address, including the street, city, state, postal code, and country. Accurate extraction of this information is vital for successful deliveries.

4. Sender & Receiver Name: The names of the sender and recipient. This data helps identify the parties involved in the shipment and facilitates communication.

5. Postal Code: The unique code assigned to a geographic area. This information is used to sort and route packages efficiently.

6. Street, City, State, Province or Area, Country: The detailed location information of the sender and recipient. This data is required for accurate delivery and customs clearance for international shipments.

7. Tracking Number or Shipping Barcode: The unique identifier assigned to a package. This code enables tracking the package’s journey through the shipping network.

8. Date: The date at which package was shipped or scheduled for delivery. This information is useful for monitoring transit times and identifying potential delays.

9. Package Quantity: The number of packages included in the shipment. This data helps ensure all packages are accounted for and delivered.

10. Package Weight: The weight of the package. This information is used to calculate shipping costs and determine appropriate handling procedures.

Common Challenges in Shipping Label Data Extraction

While data extraction from shipping labels offers immense benefits, it’s not without its challenges. The inherent variability in shipping labels, coupled with the need for accuracy and compliance, creates hurdles for businesses. Let’s delve deeper into some of the common challenges in shipping label data extraction.

1. Inconsistent Shipping Labels Formats

Shipping labels come in a wide array of sizes, layouts, and designs, varying across carriers and regions. This lack of standardization poses a challenge for data extraction tools, which need to be adaptable to different formats to accurately capture information.

2. Multiple Languages

International shipments involve labels with information in multiple languages. This adds complexity to the extraction process, as the software must be capable of recognizing and processing text in various languages.

3. Data Validation Issues

Extracted data needs to be accurate and complete to be useful. Ensuring data validation can be difficult, especially when dealing with handwritten or damaged labels.

4. Regulatory Compliance

Shipping labels contain sensitive information subject to data privacy regulations. Data extraction solutions must comply with these regulations to protect customer information and avoid legal complications.

5. High Volume Processing

Businesses dealing with large volumes of shipments face the challenge of processing vast quantities of shipping label data quickly and efficiently. Data extraction solutions need to be scalable to handle these demands.

6. Integration with Existing Systems

Extracted data needs to be seamlessly integrated with existing logistics management systems, inventory databases, and other applications. Ensuring compatibility and smooth data flow is a complex task.

Industries Benefiting from OCR-Based Shipping Label Processing

OCR’s ability to quickly and accurately extract information from shipping labels has a transformative effect across numerous sectors. As businesses seek to improve efficiency, reduce costs, and enhance customer satisfaction, OCR proves to be a valuable tool in achieving these goals.

Let’s explore some industries that particularly reap the rewards of this technology:

• E-commerce and Retail: Online retailers and brick-and-mortar stores dealing with high volumes of shipments benefit from OCR’s ability to automate data entry and streamline order fulfillment.
• Logistics and Transportation: Shipping carriers, freight forwarders, and 3PL providers rely on accurate and timely data to track shipments, manage inventory, and optimize routes. OCR-based processing enables them to achieve these goals efficiently.
• Warehousing and Distribution: Warehouse managers need real-time visibility into incoming and outgoing shipments. OCR helps automate inventory tracking, ensuring accuracy and reducing the risk of misplacement or loss.
• Manufacturing and Supply Chain: Manufacturers rely on efficient movement of goods and materials. OCR-powered data extraction from shipping labels enables them to track shipments, monitor inventory levels, and identify potential bottlenecks in the supply chain.
• Healthcare and Pharmaceuticals: The healthcare sector deals with sensitive and time-critical shipments. OCR enables quick and accurate processing of shipping label data, ensuring timely delivery of medical supplies and pharmaceuticals.

These are just a few examples, and the potential applications of OCR-based shipping label processing continue to expand across various industries. It is widely used in the following industries as well:

• Postal and Courier Services
• Third-Party Logistics (3PL) Providers
• Government and Regulatory Agencies
• Food and Beverage Industry
• Automotive Industry
• Construction and Real Estate

Key Technologies Used in Data Extraction for Shipping Labels

Modern data extraction goes beyond simple scanning. It leverages a sophisticated combination of technologies to accurately and efficiently decipher the information within shipping labels. Let’s explore some of the key technologies used in shipping labels data extraction:

• Optical Character Recognition (OCR): OCR is the cornerstone of data extraction, converting images of text into machine-readable formats. Advanced OCR systems can handle various fonts, handwriting styles, and even damaged or low-quality images.
• Machine Learning Algorithms: Machine learning algorithms enable data extraction systems to learn and improve over time. By analyzing vast amounts of shipping label data, these algorithms refine their accuracy and adapt to new label formats and variations.
• Natural Language Processing (NLP): NLP helps interpret the meaning and context of extracted text. It enables systems to identify key data fields, even if they are not explicitly labeled, and to handle variations in language and phrasing.
• Image Processing Techniques: Image processing techniques enhance the quality of label images before OCR is applied. This involves noise reduction, contrast adjustment, and skew correction, improving the accuracy of data extraction.
• Barcode Recognition: Many shipping labels contain barcodes that encode essential information like tracking numbers. Barcode recognition technology decodes these barcodes quickly and accurately.
• Cloud-based Data Processing: Cloud computing enables the processing of large volumes of shipping label data in a scalable and cost-effective manner. It also allows for easy access to data from anywhere, facilitating collaboration and real-time decision-making.
• API and SDK Integration: Application Programming Interfaces (APIs) and Software Development Kits (SDKs) facilitate seamless integration of data extraction solutions with existing business systems, ensuring smooth data flow and maximizing the value of extracted information.

8 Steps Guide to Extract Data from Shipping Labels

KlearStack is a template-free data extraction software that uses advanced technologies to process shipping labels and structure the data in the desired format. It saves thousands of hours and delivers output with up to 99% accuracy. Here is a step-by-step guide to extracting data using KlearStack:

Step 1: Register/login to the software

Once you successfully register with KlearStack as a user, you will get access to the login credentials. Upon entering these, read the terms and conditions carefully, and tick the check box. After this, you can log in to the dashboard, where all the wonders of data extraction start!

Step 2: View the dashboard to upload the documents

Once logged in, you will see the dashboard from where different documents can be uploaded. From here, you can add and process various documents, including shipping labels, bills, purchase orders, receipts, and over 12 other document types.

KlearStack also has an excellent feature that allows bulk processing of the documents, which means you can upload multiple documents and it will process all of those simultaneously giving you speedy and meticulous results.

Step 3: Upload the Shipping Label

Click on the ‘Add new’ tab. KlearStack allows you to upload documents in different formats such as Electronic PDF, Word, Excel, JPG, BMP, TIFF, PNG or Image, scanned PDF, and ZIP.

Step 4: Select the business type

Here you can browse your document and upload from sources available. You also have an option to select if the document is for a B2B or B2C transaction. This ensures better categorization and storage of your data and streamlines your further process of data collection when it comes to final reporting.

Click on Done to proceed.

Step 5: View the added document

Your most recently uploaded document will appear on top. Uploaded documents with their extracted data appear here, providing a quick snapshot of all necessary information at any given time

Step 6: Click to check the extracted data

For detailed information you can click on the uploaded document, you will see all the fields on one part of the screen and the uploaded document on the other. Here you can scroll and check all the extracted data.

If you wish to understand where the information has been picked from, simply click on that particular field and it will get highlighted on the uploaded document. If the information is incorrect, you can edit it here. This will retrain the model for future receipts from this entity. Once done, click on ‘Save’.

Step 7: Click on approve

Once you’ve verified the details are correct, click on “Approve.” The models are trained to capture data quickly and accurately.

Step 8: View the document on the dashboard

Once approved, go back to the dashboard and you will see your document there with the approval sign. Here you will be able to see all the documents and data extraction done from each.

Experience Error-Free & Efficient Shipping Label Data Extraction with KlearStack!

Manually extracting information from shipping labels is a tedious and error-prone process that hinders your logistics operations. KlearStack offers an advanced data extraction solution designed to handle large volumes of shipping labels with unparalleled accuracy and efficiency.

Our AI-powered software automates the extraction process, eliminates manual errors, and provides valuable insights to optimize your supply chain. With KlearStack, you can say goodbye to operational bottlenecks, missed opportunities, and dissatisfied customers.

Stay ahead with the latest trends and techniques in data extraction. Book a DEMO Call now to troubleshoot your problems and increase efficiency by 500%, while saving 80% on document data entry and auditing costs.

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