In this article, we will explore exactly what OCR for document processing is. We also share our 8 step OCR process for documents. Read on to learn more.
OCR for document processing refers to the use of Optical Character Recognition technology to convert scanned documents, images, or PDFs into editable and searchable digital text. This process automates the extraction of text from physical or digital images which enables efficient management of documents within various workflows.
Example: A company uses Adobe Acrobat's OCR feature to process scanned invoices, such as the HP Invoice #67892. The OCR technology extracts key details like invoice numbers, dates, and amounts, converting them into editable text within a searchable PDF.
OCR application in document processing is important for a number of reasons, some of the most common reasons include:
OCR technology significantly reduces the time spent on manual data entry and document indexing. By automatically converting scanned documents into editable formats, businesses can process large volumes of data more quickly, allowing staff to focus on more critical tasks.
OCR reduces human error associated with manual data entry and makes digital documents searchable and accessible. This accuracy is crucial for industries like legal and healthcare, where precise document handling is essential for compliance and operational integrity.
OCR helps in organizing digital archives by converting all forms of documents into a unified format that is easier to manage, search, and retrieve. This digital transformation not only saves physical storage space but also secures data against loss due to physical damage.
By automating the data extraction process, OCR reduces the need for additional personnel to handle document processing tasks. This reduction in labor costs, coupled with decreased needs for physical storage, can lead to significant financial savings for businesses.
OCR integration into document management systems allows for seamless workflows and better coordination across departments. Automated processing ensures that documents are promptly available to all relevant parties, which speeds up decision-making and improves overall business agility.
Use our 8 step OCR framework for processing documents to effectively manage your document workflows. Simply follow the steps below:
Ensure all documents are gathered and prepared for scanning. This involves cleaning the documents, removing staples or other bindings, and ensuring they are legible.
Example: A legal firm collects 300 client files, removing all clips and smoothing out creases to ensure the documents are ready for scanning using an Epson FastFoto FF-680W scanner.
Scan the documents using a high-resolution OCR-capable scanner. Set the resolution and color settings according to the document type to optimize recognition accuracy.
Example: The accounting department scans 500 invoices at 300 dpi using a Canon imageFORMULA DR-C225 II, ensuring all text is crisp and clear for subsequent processing.
Apply OCR software to convert the scanned images into editable text formats. Choose software that supports the document's language and has a high accuracy rate.
Example: Using ABBYY FineReader 15, a university processes scanned research papers to convert them into editable Word documents, preserving the layout and formatting.
Manually review and correct any errors in the OCR output. This step is crucial for maintaining data integrity and accuracy.
Example: A healthcare provider reviews OCR-processed patient records, correcting misread characters in 200 files to ensure accurate medical histories using Adobe Acrobat Pro DC’s editing tools.
Structure the OCR output into a usable format that fits the intended use, such as CSV for databases or structured text for content management systems.
Example: An e-commerce company formats 10,000 product descriptions into CSV files using Microsoft Excel, aligning data columns for easy import into their online platform.
Import the processed data into a database or document management system for easy access and retrieval.
Example: A real estate agency imports OCR-processed lease agreements into a Salesforce CRM, categorizing documents by property and lease dates for streamlined management.
Perform quality assurance checks to ensure the data is accurately processed and integrated. This may involve random checks or using software tools to spot inconsistencies.
Example: A publishing house uses custom scripts in Python to verify that 1,000 OCR-processed manuscripts have correctly aligned text blocks and that chapter titles are consistent across all documents.
Ensure that all OCR-processed documents are backed up in multiple locations and secured against unauthorized access.
Example: A financial institution backs up 5,000 processed loan applications in both cloud storage (Amazon S3) and an on-premise encrypted server to safeguard sensitive information.
Cascade Financial Solutions is a dynamic financial advisory firm committed to leveraging technology for enhanced client service and operational efficiency. Here's how they implemented our simple framework to process documents using OCR.
The administrative team at Cascade collects over 400 client investment portfolios and financial statements from the past decade. This ensures that each document is free of physical imperfections that could impair the scanning process.
Using a Fujitsu ScanSnap iX500, the document management team scans all financial statements and investment records. This sets the scanner to capture detailed text and numerical data at 600 dpi to ensure high clarity and readability.
Post-scanning, the team uses Adobe Acrobat Pro’s OCR feature to convert the images of balance sheets and income statements into editable and searchable PDF files. This step is crucial for digital archiving and further data processing.
The quality control team reviews the converted data, focusing on high-value transactions and key financial indicators to correct any OCR misinterpretations or errors, ensuring the integrity of financial data.
Processed data is organized into digital folders, categorized by client name and year. Each folder includes sub-folders for different types of financial documents like tax returns, investment summaries, and estate plans.
The IT department uploads the organized data into Cascade’s secure financial advisory platform, making it accessible to advisors. This system allows for quick retrieval of specific client financial information during consultations.
To guarantee the reliability of the digitized data, the team randomly selects accounts to compare digital entries against original documents, ensuring that critical financial figures are accurately recorded.
All digitized documents are securely backed up on encrypted cloud servers and on-premises hard drives, safeguarding against data loss and ensuring compliance with financial industry regulations concerning data protection and privacy.
We hope that you now have a better understanding of what OCR for document processing is and how to use our 8 step OCR process for documents. If you enjoyed this article, you might also like our article on OCR in accounting or our article on AI powedered OCR.