Document Understanding
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- Overview
- Getting Started
- Building Models
- Consuming Models
- ML Packages
- 1040 - ML Package
- 1040 Schedule C - ML Package
- 1040 Schedule D - ML Package
- 1040 Schedule E - ML Package
- 4506T - 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
- BillsOfLading - 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 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
- RemittanceAdvices - ML Package
- UB04 - ML Package
- Utility Bills - ML Package
- Vehicle Titles - ML Package
- W2 - ML Package
- W9 - ML Package
- Public Endpoints
- Supported Languages
- Overview
- OCR
- Other services
- Data and Security
- Licensing
Document Understanding User Guide for Modern Experience
Last updated May 9, 2024
Overview
UiPath Document Understanding leverages complex optical character recognition (OCR) and ML models, which are intelligent engines that power the language comprehension capabilities of the tool. These models are trained to discern, understand, and process a wide array of languages, transforming raw digital inputs into analyzable and understandable data.
Note: While our models are engineered to understand and process
various languages, certain scenarios may necessitate additional training to achieve the
expected level of accuracy. This applies particularly when the document data
substantially deviates from the original training dataset of the model.
For the supported languages on which the model was not pre-trained, you can train a custom model with your own dataset. However, it's important to note that this language should also be supported by the OCR engine to ensure optimal results.
