- Overview
- 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
- 990 - ML Package - Preview
- ACORD125 - ML package
- ACORD126 - 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 China - ML package
- Invoices Hebrew - ML package
- Invoices India - ML package
- Invoices Japan - ML package
- Invoices Shipping - ML package
- Packing Lists - ML package
- Passports - ML package
- Payslips - 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
- Hardware requirements
- Pipelines
- Document Manager
- OCR services
- Deep Learning
- Insights dashboards
- Document Understanding deployed in Automation Suite
- Document Understanding deployed in AI Center standalone
- Licensing
- Activities
- UiPath.Abbyy.Activities
- UiPath.AbbyyEmbedded.Activities
- UiPath.DocumentProcessing.Contracts
- UiPath.DocumentUnderstanding.ML.Activities
- UiPath.DocumentUnderstanding.OCR.LocalServer.Activities
- UiPath.IntelligentOCR.Activities
- UiPath.OCR.Activities
- UiPath.OCR.Contracts
- UiPath.OmniPage.Activities
- UiPath.PDF.Activities
Document Understanding User Guide
Use Document Manager
This page describes how to use Document Manager to label a new dataset and retrain an ML model.
Launch the created data labeling session in First Run Experience and go to the settings to configure the OCR.
Choose the OCR you intend to use in the OCR method dropdown menu. For UiPathDocumentOCR, paste the Document UnderstandingTM license key (retrieve the Document Understanding API key from the Admin > License page) and then paste the OCR URL you generated when you deployed UiPathDocumentOCR.
Configure the prelabelling with the models that you have deployed following the instructions here. Paste the model public ML Skill endpoint and the Document Understanding license key, and then click Save.
For more details, please check the documentation here: Use a predefined schema.
- Select the Import button from a Document Manager Session.
- Name the dataset and select Browse files to upload.
- Select the document you want to upload.
- Click YES.
Click to create fields to be extracted.
You can create up to 40 fields.
For this validation exercise, you can create some common invoice fields such as date, name, invoice-no, and total. Please ensure to change the content type accordingly - date (date), name (string), invoice-no (string), and total (number).
Now you can start to label the documents.
Click the Predict button on top to use the base invoice model to predict the labels for the defined fields, and correct it if the prediction is wrong.
d
for labeling date in the below example).
Use the arrow on top to switch to the next document until you have finished the validation of labels for all uploaded invoices.
- Make sure to select the correct dataset in the dataset filtering and click the Export button .
- Select Export.
- Go to Datasets under the same AI Center project, you should be able to see the exported training dataset.
Train a custom model on AI Center
- Go to Pipelines > Create new. Please select the evaluation run type, select the model package and the input dataset.
- Select the sub folder under Export as the input dataset.
- Select Create to start the pipeline. It may take 1-2 hours for the pipeline to run on CPU machines.
Go to ML Skills and create a new ML Skill.
Choose the same invoice model package created before. As we have retrained the model, now there is a new minor package version (1 vs 0). Please make sure to select the latest.
Once the ML Skill is created, please go to Modify current deployment to make the ML skill public. Switch the toggle and click Confirm.
Copy the URL of the public ML Skill for later use.
Congrats! You have now retrained an Invoice model with your own dataset and created the endpoint to access the model.