- Use Cases
Processing unstructured, complex documents and images
OCR is a mature extraction technology that works for simple use cases.
ML OCR attempts to correct OCR's limitations with machine learning.
IDP is a different approach that is not OCR-based.
OCR and ML OCR assumes documents are structured and consistent
IDP assumes documents will change and can process complexity, unstructured layouts and noisy documents.
OCR and ML OCR use templates to tell the OCR where to extract the data. Templates are notoriously hard to scale and requires significant maintenance.
IDP is template-free. The system understands what to extract based on AI processing. Instead of coded templates, IDP learns what and where to extract. And IDP learns overtime and improves its performance without need to template modifications.
✔️ IDP wins on scalability
IDP is built on an AI Platform that provides more functionality than just converting an image into text that an OCR does.
IDP's functionality includes:
✔️ IDP wins on functionality
IDP includes a customer control center through which the IDP application is managed. A business analyst can add/change documents and extraction points and manage application performance.
OCR and ML OCR use templates which can be difficult to manage.
✔️ IDP wins on manageability