In this article, you will learn what OCR data capture is and its benefits for efficient data processing. We also share our simple OCR process to capture data. Read on to learn more.
OCR (Optical Character Recognition) data capture is a technology that automatically extracts text from scanned documents, images, or PDFs, converting it into editable and searchable data. It streamlines data entry by eliminating the need for manual typing, improving accuracy and efficiency.
Example: Using OCR data capture, Lido can automatically extract and digitize text from a scanned invoice, such as product codes and amounts, allowing for quick entry into an ERP system without manual input.
Use our 8 step OCR process for capturing data efficiently manage your data. Simply follow the steps below.
The first step in OCR data capture involves obtaining a high-quality image or document, either by scanning or uploading it into the OCR system. This image serves as the foundation for text extraction.
Example: A law firm scans a contract to upload it into the LexiScan Legal Suite OCR software by JurisTech Inc., preparing it for text extraction and legal review.
The uploaded image undergoes preprocessing to optimize it for accurate text recognition. This step includes techniques like noise reduction, contrast enhancement, and skew correction to improve readability.
Example: In a scanned architectural blueprint, BlueprintOCR Max by DesignWare Ltd. adjusts the contrast and removes background noise, ensuring that technical specifications are clearly recognized and captured.
The OCR system then identifies and distinguishes text from non-text elements within the image. This step involves locating blocks of text that need to be processed while ignoring images and graphics.
Example: The ClearText Pro OCR system by InfiniSoft Technologies identifies and isolates text from a scanned newspaper article, separating the headlines, body text, and image captions for accurate recognition.
Once text areas are identified, the OCR software analyzes each character using pattern recognition and machine learning algorithms, converting the detected text into editable digital content.
Example: The DataScan Alpha software by ProDigit Systems converts the product code ‘VTR456’ from a scanned inventory sheet into digital text for entry into a warehouse management system like StockMaster Plus.
After recognizing the text, the system performs post-processing to correct errors and refine the output. This step may include spelling correction, formatting adjustments, and validating text against a dictionary.
Example: The ScanXpert Vision 3000 software corrects a misrecognized word ‘Reciept’ to ‘Receipt’ in a scanned document before it is finalized for digital storage.
The recognized text is then exported into the required format, such as a Word document, Excel spreadsheet, or a database entry. This step ensures the data can be easily integrated into existing workflows.
Example: Text from a scanned recipe book is exported into a searchable PDF using DocuForm OCR by PaperTrail Solutions, allowing for easy access and digital storage in a culinary database.
Before utilizing the captured data, a verification process is carried out where users review and validate the accuracy of the OCR results. This step ensures the integrity of the data before it is used.
Example: A logistics manager uses LexiScan Legal Suite to check the extracted data from a batch of scanned shipping documents, confirming that all tracking numbers and delivery addresses are accurately captured.
The final step is integrating the OCR data with other software systems for further processing or automation. This allows the captured data to be efficiently used in applications like ERP systems or customer relationship management (CRM) platforms.
Example: Data extracted from scanned invoices using ScanXpert Vision 3000 is automatically fed into the BizTrack ERP system by Nexus Enterprises for streamlined accounts payable processing.
Here are some of the benefits of using OCR data capture:
OCR data capture automates the extraction of text from documents, reducing the need for manual data entry. This streamlining saves time, minimizes human error, and allows employees to focus on higher-value tasks.
By eliminating manual entry, OCR data capture significantly reduces the risk of errors. The technology ensures that data is captured precisely as it appears in the source document, enhancing overall data integrity.
OCR data capture converts physical documents into digital formats, making them easier to store, search, and retrieve. This digitalization improves document management, enabling quick access to information and more efficient workflows.
With OCR, businesses can maintain accurate and easily accessible records, which is crucial for compliance with industry regulations. The ability to quickly retrieve and verify documents supports auditing processes and reduces compliance risks.
By automating routine tasks, OCR data capture allows employees to focus on more strategic activities. This increase in productivity leads to better utilization of resources and more efficient business operations.
OCR data capture seamlessly integrates with existing business systems, such as ERP or CRM platforms. This integration allows for the automatic transfer of captured data into these systems, streamlining operations and improving data consistency.
We hope that you now have a better understanding of what OCR data capture is and how it works. If you enjoyed this article, you might also like our article on text recognition vs. OCR or our article on OCR automation.