In this article, you will learn exactly what OCR data extraction is and why it’s essential for businesses. We also explore its benefits and share our simple 10 step process for effective implementation. Read on to learn more.
OCR (Optical Character Recognition) data extraction is the process of converting different types of documents, such as scanned paper documents, PDF files, or images, into machine-readable text. This technology identifies and extracts text from images and documents, making it possible to edit, search, and manage this information digitally.
Example: A global retail company uses OCR data extraction to convert thousands of paper invoices into searchable digital files, significantly reducing manual data entry and streamlining their operations.
OCR for data extraction is important for a number of reasons, some of the most common reasons include:
OCR data extraction automates the process of converting physical documents into digital data, reducing the time and effort required for manual data entry.
Manual data entry is prone to errors, while OCR minimizes these errors by automatically recognizing and extracting text.
By automating data extraction, businesses can reduce labor costs associated with manual data entry and document management.
Use our simple 10-step process of extracting data through OCR to accurately capture and organize essential information from scanned documents. Simply follow the steps below:
Prepare the image by enhancing contrast, resizing, or removing noise to improve OCR accuracy. These adjustments make text recognition clearer and more accurate for the OCR model.
Example: Increase the contrast on a receipt image to better define product names like "Item 123" and prices like "12.99", making the text stand out for cleaner extraction.
Choose the OCR tool or API that best fits your needs based on accuracy, language support, and data security requirements. Evaluate both open-source and commercial options to ensure compatibility with your project.
Example: Use Tesseract OCR to extract data from a scanned invoice listing items like "Product A - 45.00" and "Product B - 15.75".
Specify the data fields you want to capture, such as names, dates, or amounts, to streamline the OCR process. Defining these fields helps in targeting specific data rather than capturing all text.
Example: Extract "Invoice Number 4567" and "Total Amount 200.50" from a purchase order by specifying these fields as targets.
Run the OCR on the image to convert visual data into text. This step typically involves submitting the image to the OCR engine and retrieving raw text output.
Example: Use OCR to convert a scanned bill with "Transaction ID: 78910" and "Total: 150.00" into editable text for further processing.
Clean and format the extracted text by removing unwanted characters or correcting errors. This step ensures the output is usable and aligns with data standards.
Example: Remove extra spaces and correct misinterpreted characters in "Pr0duct C - 9.99" to accurately read as "Product C - 9.99".
Review the extracted text for accuracy, checking against the original document to ensure fidelity. Automated validation rules or manual checks can be applied based on the complexity of the data.
Example: Compare the extracted amount "200.50" to the original document's amount field to confirm no errors in extraction.
Arrange the cleaned data into a structured format, such as CSV or JSON, to prepare it for analysis or integration. Structured formatting facilitates easy storage and retrieval.
Example: Organize extracted product entries like "Product X - 25.00" and "Product Y - 30.00" into a CSV file for inventory management.
Save the structured data in a database or other repository for secure storage and future access. Ensure that the storage solution is compatible with your data usage needs.
Example: Insert the data fields "Product Z" and "Price: 55.00" into a SQL database for historical tracking.
Set up an automated workflow to continuously process incoming documents for OCR extraction. Automation can reduce manual intervention and improve data processing speed.
Example: Schedule a script to auto-extract product prices from daily sales receipts, such as "Item 101 - 20.00" and "Item 102 - 35.50".
Regularly assess the OCR accuracy and update preprocessing, extraction, and validation techniques. Continuous improvement helps maintain high-quality data extraction as document types and formats evolve.
Example: Test adjustments for better recognition of handwritten amounts like "120.45" and "89.30" on varying invoice types.
DataSync Solutions is a premier logistics firm specializing in streamlined shipping and delivery management for high-volume clients. Here's how they implemented our simple OCR data extraction technology process.
DataSync adjusts a scanned delivery form’s contrast, making details such as “Tracking No: 543210” and “Delivery Address: 123 Maple Ave” clearer for accurate data capture.
DataSync chooses ABBYY FineReader due to its reliability in recognizing alphanumeric codes, ensuring that tracking IDs like “SHIP7890” are consistently captured without errors.
DataSync targets “Shipped Date,” “Receiver Address,” and “Tracking Number” fields from shipping forms, allowing them to automate the entry of only critical shipment data.
When DataSync runs OCR on a document, it quickly captures and converts “Weight: 50 lbs” and “Destination: Los Angeles” into structured text fields.
DataSync removes errors, like misinterpreted characters or extra spaces, to maintain data consistency across all records. If “Los Ang3les” is misrecognized, DataSync corrects it to “Los Angeles” to maintain uniformity across address fields.
DataSync cross-references the extracted tracking number “ABC12345” with the original document to ensure there are no discrepancies in the data.
DataSync organizes entries like “Order ID: 67890” and “Delivery Date: 10/15/2023” in a CSV file, allowing them to track each shipment’s progress efficiently.
DataSync uploads records with fields like “Shipment ID: XZ1234” and “Warehouse Location: Miami” into their database for seamless integration with their logistics software.
With automation in place, DataSync processes daily shipping records like “Package ID: ZT5678” and “Delivery Time: 2:00 PM” without requiring manual uploads.
DataSync tweaks OCR settings to improve the recognition of labels like “Express” and “Standard,” ensuring these shipping categories are consistently extracted across all documents.
We hope you now have a better understanding of what OCR technology for data extraction is and how to implement our 10-step process to improve your document management.
If you enjoyed this article, you might also like our article on is OCR considered AI or our article on OCR for data entry.