| Purpose | Extracts text from images and scanned documents. | Automates processing of structured and unstructured documents from a variety of inputs (PDF, image, email, etc.) |
| Accuracy | Around 95% for clear, well-formatted text, dropping to 50-70% with handwriting and poor image quality. | Up to 99.5% as defined by your business needs, for both printed and handwritten text. |
| Speed | Faster processing, but lower accuracy and more manual effort to correct mistakes. | Slower initial processing, but increased accuracy improves overall processing times. |
| Flexibility | Used for well-structured documents where you know the data that needs to be extracted and where to find the data within the document. | Handles a wide range of document types and formats, from structured forms to fully unstructured documents like contracts or emails. |
| Customization | Text extraction only. Often requires additional tools for preprocessing and postprocessing. | Adaptable to any business process, capabilities go well beyond extraction, helping customers act on data. |
| Human-in-the-Loop | None. Errors must be corrected later, often in another system. | Machine learning identifies when human input is necessary to ensure the highest accuracy. |
| Templates | Requires predefined templates for each specific document variation, often requiring IT or 3rd party support. | Template-free. ML handles document variability by learning from day to day processing. |
| Learning Capabilities | None. Needs constant maintenance to avoid making the same mistakes over and over. | Uses human feedback to finetune ML models, requiring less human intervention over time. |
| Adding New Use Cases | Between 2-4 weeks, depending process complexity. | Normally 1-2 days, depending on process complexity. |
