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OCR vs. IDP: Which is ideal for extracting data from documents?
The ability to extract key data points from a document accurately and timely is invaluable for businesses as decision-making depends on it. In a bid to enhance their existing document management and processing workflows, business and data leaders often engage in the OCR vs. IDP debate. Both IDP and OCR help speed up information extraction from documents, but this is where their similarities end. And that means they’re more different than they’re similar. The question, therefore, is, to what extent?
What is OCR?
The acronym OCR stands for optical character recognition. As the name suggests, it identifies characters (glyphs or words) that are printed, typed, or handwritten from images, scanned documents, and PDF files and converts them into machine-readable text. OCR uses a combination of technologies and techniques, with computer vision and pattern recognition being the primary methods for analyzing visual data and identifying patterns.
From an organization’s perspective, OCR accelerates document data extraction to support business processes, such as processing a large number of invoices quickly.
How does OCR work?
Let’s say you receive a document, or scan one yourself, and import it into the OCR software. It translates the visual information so that your computer can understand the characters, numbers, and words it contains. This involves a couple of key steps: first, the OCR software processes the image to detect and isolate the text. Then, it identifies the shapes of individual characters and matches them to its internal database, essentially reading the text.
To identify patterns, it compares the shapes in the image to known shapes for letters and numbers, often using machine learning to improve accuracy over time. But here’s the catch: OCR, on its own, is literal. It doesn’t understand context or meaning. If you scanned an invoice with OCR, it would pull all the text—including irrelevant parts like disclaimers—leaving you to manually sift through the data to find what you need manually.
What is IDP?
IDP stands for intelligent document processing. It gives information extraction a whole new meaning as it automates the entire document management and processing workflow. IDP uses a combination of OCR, ML, artificial intelligence (AI), and natural language processing (NLP) to extract data all the while understanding and validating it. Unlike standalone OCR tools, IDP software provides a unified solution for document data extraction without having to integrate multiple solutions or frameworks.
How does IDP work?
After scanning for and extracting text using OCR, IDP applies ML algorithms and NLP techniques to make sense of it.
NLP provides context around the words helping the system identify key information to extract such as names, dates, invoice numbers, or any other field relevant to the task. Machine learning models train the system to adapt to different document types and layouts, even if they are inconsistent or unstructured. It allows IDP to classify the documents correctly and extract requisite data.
The extracted data, which is now meaningful, is validated for accuracy with human-in-the-loop verification for critical fields, and integrated into downstream systems like databases, ERPs, and CRMs, or workflows like automated invoice processing or accounts payable (AP) automation.
Learn more: What is intelligent document processing (IDP)?
OCR vs. IDP: all the differences at a glance
At the basic level, OCR is a tool for simple text extraction, while IDP is a comprehensive solution for document automation, making IDP a better choice for businesses looking to scale and streamline their processes. Here are all the differences between IDP and OCR:
Different use cases OCR and IDP cater to
Understanding the specific use cases each technology is suited to is crucial to selecting the right tool, especially since the objective is to streamline document management workflows by reducing manual effort and improving accuracy. Below, we explore the key applications of both technologies across industries:
Use cases of OCR
OCR caters to simpler use cases:
- Converting handwritten or printed patient forms into digital records, making it easier for healthcare providers to store and retrieve patient data
- Extracting details like invoice numbers and amounts from supplier invoices in a consistent format to log and manage payments easily
- Digitizing books, articles, and other content and making them searchable and accessible online
- Enhancing form processing with OCR to quickly extract data from contracts, surveys, and other documentation
Use cases of IDP
Unlike OCR, IDP caters to more complex and dynamic document processing needs:
- Invoice and AP automation by extracting and validating important data, such as invoice numbers, vendor names, purchase order references, and totals, even from varying invoice layouts
- Automating the processing of loan forms, bank statements, and supporting documents in finance
- In insurance, IDP automates the extraction and classification of claim forms, policy documents, and supporting evidence
- Processing patient intake forms, lab results, or medical claims to integrate structured data into electronic health records (EHRs)
Should you choose OCR or IDP?
Depending on the size of your organization and the use case, the question may seem redundant, particularly since IDP includes OCR as one of many underlying technologies for document processing. So, if your needs are likely to grow or evolve in the near future, it’s worth considering IDP from the start. However, OCR has some benefits of its own: it’s cost-effective, straightforward to implement and maintain for simple tasks, and is not affected by AI flaws, like hallucination.
Here are some factors to consider when deciding between IDP or OCR:
When should you choose OCR?
OCR can prove to be a simple and cost-effective solution if you’re a small business and the following factors apply:
- You only need to digitize documents for more accessible storage, retrieval, and archival
- You handle structured documents with consistent layouts, like forms or invoices
- Your document processing requirements don’t go beyond basic text extraction
- You already have or plan to incorporate additional software or integrations to validate data
- You have the means to manage the manual intervention required to organize data and handle errors
When should you opt for IDP?
On the contrary, IDP is the better choice if you work in a large organization and the following factors apply:
- In addition to structured documents, you regularly deal with high volumes of unstructured and semi-structured documents, and your business operations demand accuracy, scalability, and adaptability to new document types over time
- You’re looking for a long-term, future-proof solution to streamline document processing at scale
- You have advanced use cases like processing invoices from multiple vendors or extracting key clauses from contracts, and you need an automated solution to extract data with context, including field-specific classification and validation
- Automation is critical to your workflows, and you need to integrate extracted data into other systems or business applications
- Budget is not a constraint, and you’re ready to invest in a solution that would provide greater ROI by reducing costs and improving operational efficiency
Conclusion
To summarize, think of OCR as a starting point for document data extraction, while IDP is the future-proof choice for automated document processing and management with efficiency and scalability.
Once you’ve decided whether you need IDP or OCR, the next step is to integrate an automated platform—one that simplifies and accelerates document processing—into your data and document management stack. If you’re ready to discuss your document processing use case, contact Astera today.
IDP vs. OCR: Frequently Asked Questions (FAQs)
Is IDP the same as OCR?
No, IDP is not the same as OCR. OCR is a technology focused solely on extracting text from scanned or image-based documents. On the other hand, IDP uses OCR in combination with AI technologies like machine learning and natural language processing to not only extract text but also understand, validate, and organize data, making it suitable for more complex document processing tasks.
What is replacing OCR?
OCR is not being entirely replaced, but it is being enhanced by more advanced technologies like IDP, which allows for greater accuracy and adaptability, especially when dealing with unstructured and semi-structured documents.
How are IDP and OCR different from RPA?
OCR and IDP focus on extracting and processing data from documents, whereas robotic process automation (RPA) automates repetitive tasks across systems, such as data entry, report generation, and interactions between applications.
What is the difference between OCR and ICR?
OCR is used for recognizing printed text, while ICR, short for intelligent character recognition, is a more advanced version of OCR that can recognize handwritten text and varying fonts. Compared to OCR, ICR adapts to different handwriting styles and improves accuracy when dealing with less structured text.
Authors:
Khurram Haider
