- Overview
- Getting started
- Activities
- Insights dashboards
- Document Understanding Process
- Quickstart tutorials
- Framework components
- ML packages
- Overview
- Document Understanding - ML package
- DocumentClassifier - ML package
- ML packages with OCR capabilities
- 1040 - ML package
- 1040 Schedule C - ML package
- 1040 Schedule D - ML package
- 1040 Schedule E - ML package
- 1040x - ML package
- 3949a - ML package
- 4506T - ML package
- 709 - ML package
- 941x - ML package
- 9465 - ML package
- ACORD131 - ML package
- ACORD140 - ML package
- ACORD25 - ML package
- Bank Statements - ML package
- Bills Of Lading - ML package
- Certificate of Incorporation - ML package
- Certificate of Origin - ML package
- Checks - ML package
- Children Product Certificate - ML package
- CMS 1500 - ML package
- EU Declaration of Conformity - ML package
- Financial Statements - ML package
- FM1003 - ML package
- I9 - ML package
- ID Cards - ML package
- Invoices - ML package
- Invoices Australia - ML package
- Invoices China - ML package
- Invoices Hebrew - ML package
- Invoices India - ML package
- Invoices Japan - ML package
- Invoices Shipping - ML package
- Packing Lists - ML package
- Payslips - ML package
- Passports - ML package
- Purchase Orders - ML package
- Receipts - ML Package
- Remittance Advices - ML package
- UB04 - ML package
- Utility Bills - ML package
- Vehicle Titles - ML package
- W2 - ML package
- W9 - ML package
- Other Out-of-the-box ML Packages
- Public endpoints
- Traffic limitations
- OCR Configuration
- Pipelines
- OCR services
- Supported languages
- Deep Learning
- Licensing
Machine Learning Extractor
The Machine Learning Extractor is a data extraction tool using machine learning models in order to identify and report on data targeted for data extraction.
This activity is the companion of UiPath® Document UnderstandingTM models, as the means to consume such models within your workflows.
The ML approach is strongly recommended for structured or semi-structured documents in which layouts of different document providers vary greatly. Given its machine learning approach, the extractor uses a trained machine learning model, that learns and can then infer values for the targeted fields, even from documents and layouts it has never seem before. In other words, if documents do not follow a text or layout pattern, the Machine Learning Extractor may be a good option for your use case.
The Machine Learning Model can be used in multiple ways:
- with one of UiPath's public Document Understanding endpoints, if you wish to use generic models targeting certain document types; or
- with custom trained machine learning models starting from the UiPath Document Understanding available models.
This extractor can be trained / re-trained. See the Machine Learning Extractor Trainer section for details.
You need to use
- one of UiPath's public Document Understanding endpoints for data extraction, or
- machine learning models hosted in AI Center in Automation Cloud, or
- machine learning models hosted in AI Center on-prem, but licensed through Automation Cloud, you need to use your Automation Cloud Document Understanding API Key.
To use the Machine Learning Extractor with on-prem licensing, you need to host your Document Understanding models in your AI Center on-prem (air-gapped install) instance.
If the endpoint you are using is licensed through Automation Cloud, you need to provide your Cloud Document Understanding API Key.
If you are using the Machine Learning Extractor with either a UiPath Document Understanding public endpoint, or with a public ML Skill in AI Center, then you need to configure the Endpoint argument of the activity with the corresponding URL.
If you are using the Machine Learning Extractor with a deployed ML Skill, then you need to configure the ML Skill argument of the activity with the correct selection from your AI Center hosted ML skills list.
If you try to set both options, an error is displayed - either in the Configuration Wizard, or in the workflow directly:
