What are the differences between rule based and model based extractions?

What are the differences between rule based and model based extractions?
A . The rub-based extraction uses methods like regex extractor and form extractor on semi-structured documents while the model based extraction uses the forma Al and machine learning on documents with fixed formal
B . The model-based extraction is used for documents with a fixed format, relies on regular expressions and templates and ensures high accuracy for already known documents The rule-based extraction is used for semi-structured documents and relies on pre-trained models as we" as on custom models
C . The rule-based extraction is used for documents with a fixed format relies on rules (like regular expressions) and templates and ensures high accuracy for already known documents The model-based extraction is used tor semi-structured documents and relies on pre-trained models (like invoices receipts purchase orders etc) as well as on custom models
D . The rule-based extraction uses methods like regex extractor and forms Al. on documents with a fared format, while the model-based extraction uses the machine learning extractor on semi structured documents

Answer: C

Explanation:

The rule-based extraction and the model-based extraction are two different methods of data extraction that target different types of documents. The rule-based extraction is suitable for structured documents that have a fixed format and layout, such as forms, tax returns, or certificates. This method relies on rules (such as regular expressions) and templates (such as position or occurrence patterns) to identify and extract the data of interest from the document. The rule-based extraction ensures high accuracy and speed for already known documents, but it requires manual configuration and maintenance of the rules and templates, and it cannot handle variations or changes in the document format. The model-based extraction is suitable for semi-structured documents that have varying formats and layouts, but contain similar types of information, such as invoices, receipts, or purchase orders. This method relies on pre-trained models (such as machine learning or artificial intelligence models) or custom models (such as user-defined models) to analyze and extract the data of interest from the document. The model-based extraction can handle variations and changes in the document format, and it can learn from feedback and improve over time, but it requires training data and validation, and it may not achieve the same level of accuracy and speed as the rule-based extraction for some documents.

Reference: Data Extraction Overview – UiPath Document Understanding Document Processing with Improved Data Extraction | UiPath Document Understanding – Machine Learning Extractor – UiPath