- Getting Started
- Introduction
- Language Support
- Document Understanding Process: Studio Template
- Document Understanding Configuration Checklist
- AI Center Relation to Document Understanding
- Framework Components
- Taxonomy
- Taxonomy Overview
- Taxonomy Manager
- Taxonomy Related Activities
- Digitization
- Digitization Overview
- OCR Engines
- Digitization Related Activities
- Document Classification
- Document Classification Overview
- Configure Classifiers Wizard of Classify Document Scope
- Keyword Based Classifier
- Intelligent Keyword Classifier
- FlexiCapture Classifier
- Machine Learning Classifier
- Document Classification Related Activities
- Document Classification Validation
- Document Classification Validation Overview
- Classification Station
- Document Classification Validation Related Activities
- Document Classification Training
- Document Classification Training Overview
- Configure Classifiers Wizard of Train Classifiers Scope
- Machine Learning Classifier Trainer
- Document Classification Training Related Activities
- Data Extraction
- Data Extraction Overview
- Configure Extractors Wizard of Data Extraction Scope
- RegEx Based Extractor
- Form Extractor
- Intelligent Form Extractor
- Machine Learning Extractor
- FlexiCapture Extractor
- Data Extraction Related Activities
- Data Extraction Validation
- Data Extraction Validation Overview
- Validation Station
- Data Extraction Validation Related Activities
- Data Extraction Training
- Data Extraction Training Overview
- Configure Extractors Wizard of Train Extractors Scope
- Machine Learning Extractor Trainer
- Data Extraction Training Related Activities
- Data Consumption
- Data Consumption Overview
- Data Consumption Related Activities
- ML Packages
- About ML Packages
- Hardware Requirements
- Supported languages
- OCR
- ML Packages
- Other services
- OCR Configuration
- Install and Use Intelligent Form Extractor
- Pipelines
- About Pipelines
- Training Pipelines
- Evaluation Pipelines
- Full Pipelines
- Fine-tuning
- The Auto-Fine-tuning Loop (Public Preview)
- Data Manager
- About Data Manager
- Access Data Manager
- Create and Configure Fields
- Use a Predefined Schema
- Import Documents
- Label Documents
- Search Documents
- Export Documents
- Checkboxes and Signatures
- OCR Services
- OCR Services
- Document Understanding deployed in Automation Suite
- Install and Use
- First Run Experience
- Deploy UiPathDocumentOCR
- Deploy an Out-of-the-box ML Package
- ML Packages Offline Installation
- Use Data Manager
- Use the Framework
- Document Understanding deployed in AI Center standalone
- Install and Use
- First Run Experience
- Deploy UiPathDocumentOCR
- Deploy an Out-of-the-box ML Package
- ML Packages Offline Installation
- Use Data Manager
- Use the Framework
- Deep Learning
- Training High Performing Models
- Licensing
- Public Endpoints
- API Key
- Cloud and On-Prem Usage
- Metering & Charging Logic
- References
- Activities Packages
- UiPath.Abbyy.Activities
- UiPath.AbbyyEmbedded.Activities
- UiPath.DocumentUnderstanding.ML.Activities
- UiPath.DocumentUnderstanding.OCR.LocalServer.Activities
- UiPath.IntelligentOCR.Activities
- UiPath.OCR.Activities
- UiPath.OCR.Contracts
- UiPath.DocumentProcessing.Contracts
- UiPath.OmniPage.Activities
- UiPath.PDF.Activities
Document Understanding User Guide
Digitization is the process of obtaining machine readable text from a given incoming file, so that a robot can then understand its contents and act upon them. It is the first step applied on files that need to be processed through the Document Understanding framework.
The digitization step has two outputs:
- the text from the processed file, stored in a string variable, and
- the Document Object Model of that file - JSON object containing basic information such as name, content type, text length, the number of pages, as well as detailed information such as page rotation, detected language, content and coordinates for every word identified in the file.
In the Document Processing Framework, digitization is performed using the Digitize Document activity.
What Digitization Is Not
Even though related, the digitization step is not OCR.
In many cases, the files that need to be processed are native PDF files (not scanned), that can be read programmatically by the robot without applying OCR.
When Is OCR Used in Digitization
The Digitize Document activity requires, as part of its configuration, the selection of an OCR engine - so that, at need, it can be used, but only executes OCR on
- files that are images
- supported images formats are .png, .gif, .jpe, .jpg, .jpeg, .tiff, .tif, .bmp
- for multi-page TIFF files, OCR is applied for each page
- PDF pages that
- do not expose any machine readable content
- contain images that cover a significant area of the page.
OCR is also applied, always, if the Digitize Document activity is configured with the ForceApplyOCR flag set to True. This option is usually recommended for use cases in which a significant percentage of files seem to contain native content, but the natively read content does not correspond to what a user can see in those files.
How to Choose Your OCR Engine
As each use case has its own particularities, it is strongly recommended to test all available OCR Engines with different settings, in order to determine which one works best for your project. Another recommendation is to pay particular attention to the OCR engine arguments, such as Profile,Scale,Language etc. (may vary from one engine to another), so that you identify the best settings for each use case.