AI-assisted data capture and traditional OCR (Optical Character Recognition) applications are both capable of extracting data from various sources such as documents, images, or scanned files. However, there are some significant performance differences between these two approaches. In terms of AI-assisted data capture vs. traditional OCR, let’s take a look at a comparison of the two applications to help you evaluate which solution may be best for your operation:
Accuracy
AI-assisted data capture systems generally outperform traditional OCR applications in terms of accuracy. Traditional OCR relies on predefined rules and templates, which may struggle with complex layouts, handwriting, or poor-quality documents. On the other hand, AI-assisted data capture utilizes machine learning algorithms to continuously improve recognition accuracy, adapt to new patterns, and handle variations in data presentation.
Flexibility
AI-assisted data capture offers greater flexibility compared to traditional OCR applications. With AI, the system can learn from a wide range of document types, formats, and languages, making it adaptable to different industries and use cases. Traditional OCR, on the other hand, often requires specific configurations and customization to handle different document types effectively.
Data Extraction
AI-assisted data capture systems can extract not only structured data but also unstructured information from documents. It can understand context and relationships, and even extract data from tables, check boxes, and handwriting. Traditional OCR, while effective at extracting structured data, may struggle with complex layouts or unstructured content, limiting its capabilities.
Automation
AI-assisted data capture enables higher levels of automation. By leveraging AI algorithms, it can automatically classify and extract data from large volumes of documents without extensive manual intervention. Traditional OCR applications may require more manual effort in setting up templates, defining rules, and handling exceptions.
Learning and Improvement
AI-assisted data capture systems have the ability to learn and improve over time. They can analyze user feedback, make adjustments based on new data, and continuously enhance recognition accuracy. Traditional OCR applications typically lack this adaptive capability and require manual updates to improve performance.
Integration and Scalability
AI-assisted data capture systems can be easily integrated into existing workflows and systems through APIs, making them more scalable and compatible with various platforms. Traditional OCR applications may require additional development efforts for seamless integration.
Conclusion
In terms of AI-assisted data capture vs. traditional OCR, overall, AI-assisted data capture surpasses traditional OCR applications in terms of accuracy, flexibility, data extraction capabilities, automation, learning, and scalability. It offers a more intelligent and efficient solution for extracting and processing data from a wide range of sources, consequently leading to improved productivity and accuracy in data capture tasks.
These differences are magnified in environments like accounts payable where large volumes of documents of varying formats and quality have made the data capture process expensive, time-consuming, and manually intensive. While traditional OCR applications have provided some relief the increased levels of performance from AI-assisted data capture solutions can drive game-changing efficiencies in AP and other document and transaction-intensive operations.
Contact ICG today to start a conversation on how ICG’s AI-assisted data capture solutions can deliver immediate value to your financial back-office. Or you can request a demo of one of our other solutions and see for yourself how your company can take advantage of the power of AI.