- Introducing ABBYY FlexiCapture
- Installing and running the program
- ABBYY FlexiCapture architecture
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Program settings
- ABBYY FlexiCapture Setup
- Multitenancy
- Creating a project
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Document Definitions
- Creating fixed Document Definitions
- Creating Document Definitions for semi-structured documents
- Document Definitions without automatic fields extraction
- Document sets
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Document Definition fields
- Text entry fields
- Checkmarks
- Checkmark groups
- Barcodes
- Pictures
- Tables
- Field group
- Service fields
- Index fields
- Link to an existing field
- Fields without a region
- Creating a field with a non-rectangular region
- Fields with several instances
- Fields with several regions
- How to change a field name
- Copying, moving, deleting fields
- Exclusion of a region from recognition
- Document Definition Wizard
- Editing and publishing a Document Definition
- Creating Document Definitions
- Document Definition properties
- Properties of a Document Definition section
- Rule validation
- Export settings
- Configuring data presentation in the document window
- Testing Document Definitions
- Localizing a Document Definition
- Classification
- Field extraction training
- Operating a configured project
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ABBYY FlexiCapture for Invoices
- Features of ABBYY FlexiCapture for Invoices
- How to capture invoices
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How to set up an invoice capture project
- Country and language settings
- Connecting vendor and business unit databases
- Data export settings
- The status of documents in ABBYY FlexiCapture for Invoices projects
- Training ABBYY FlexiCapture for Invoices
- Rules
- Capturing additional invoice fields
- Purchase Order Matching
- Enabling additional program features for operators
- Using multiple Document Definitions
- Editing invoice processing settings in XML files
- Updating the Document Definition for invoices
- Tax systems
- Specifications
- Capturing receipts
- Capturing purchase orders
- Using NLP to process unstructured documents
- ABBYY FlexiCapture interface
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Appendix
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Using scripts in ABBYY FlexiCapture
- Specifics of scripts written in .Net languages
- External assemblies
- Object model
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Scripts for customizing processing stages
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Types of scripts
- Script rule
- Autocorrection script
- Export script
- User script (custom action)
- Document assembly script
- Custom recognition script
- Stage rule
- Processing scripts
- Data set update script
- Data set validation scripts
- Document classification script
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Event handlers
- Batch created
- Batch deleted
- Batch parameter change
- Batch structure change (page added/page deleted/document added/document deleted)
- Pages moved
- Batch opened/closed
- Batch integrity check
- Document parameter changed
- Document state changed
- Export completed
- Script that is run after rule checks
- Before matching
- Field verification request
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Objects
- IActionResult
- IAssemblingError
- IAssemblingErrors
- IBatch
- IBatchCheckResults
- IBatchItem
- IBatchItems
- IBatchTypeClassifier
- IBatchTypeClassifierResult
- IBinarizationParams
- IBoxedBoolean
- ICharacterParams
- ICharactersParams
- ICheckmarkGroupValue
- ICheckmarkValue
- IDataSet
- IDataSetQuery
- IDataSetRecord
- IDocument
- IDocuments
- IDocumentExportResults
- IDocumentsExportResults
- IDocumentDefinitionInfo
- IDocumentDefinitionInfoArray
- IEditablePictureObject
- IExportFieldsToRedact
- IExportImageSavingOptions
- IField
- IFieldRegion
- IFieldRegions
- IFields
- IFlexiCaptureTools
- ILocalContrastParams
- IMatchedSectionInfo
- IMatchingInfo
- IPage
- IPageClassificationResult
- IPages
- IPictureObject
- IPictureObjectsInfo
- IPrincipal
- IPrincipals
- IProcessingCallback
- IProject
- IProperties
- IProperty
- IPropertyModificationInfo
- IRecordCheckResult
- IRecordset
- IRect
- IRects
- IRoutingRuleResult
- IRuleContext
- IRuleError
- IRuleErrors
- IRuleTag
- IRuleTags
- IScriptBinaryAttributes
- IScriptDefinitionContext
- ISectionDefinitionInfo
- ISectionDefinitionInfoArray
- IShadowsHighlightsParams
- IStageInfo
- IUserAttachment
- IUserAttachments
- IUserSessionInfo
- IValue
- IVARIANTArray
- TAssemlingErrorType
- TBatchItemType
- TColorToFilter
- TExportFieldType
- TExportType
- TImageCompressionType
- TPageClassificationType
- TPdfAVersion
- TPdfDocumentInfoType
- TPdfTextSearchAreaType
- TPrincipalType
- TProcessingPriority
- TPropertyType
- TRuleErrorType
- TStateType
- Sample scripts
- Internal names of recognition languages
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Types of scripts
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Scripts for processing interface events
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Event handlers
- On Document Closed
- On Project Closed
- On Activate Document
- On Field Control Activate
- On Return From Task
- On User Command
- On Field Control Deactivate
- On Closing Document
- On Task Close
- On Closing Project
- On Region Change
- On Task Window Mode Changed
- On Open Document
- On Task Window Create
- On Task Reject
- On Region Control Draw
- On Task Send To Stage
- On Text Field Validating
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Objects
- IBoolean
- IBoxedFieldControl
- IDocumentEditor
- IDocumentItem
- IDocumentItems
- IDocumentsCollection
- IDocumentsWindow
- IDrawContext
- IErrorControl
- IErrorControls
- IErrorsWindow
- IFieldControl
- IFieldRegionControl
- IFieldRegionControls
- IFormWindow
- IImageWindow
- IMainMenu
- IMainWindow
- IMenu
- IMenuItem
- IPageControl
- IPageItem
- IPageItems
- IPagesCollection
- IPoint
- ISelection
- IShellRational
- IShellRect
- IShellRects
- ITaskWindow
- ITextEditor
- IToolbar
- IToolbarButton
- IToolbars
- TCommandBarType
- TCommandID
- TDockingType
- TDocumentState
- TErrorType
- TSelectionType
- TTaskWindowMode
- TTextSize
- TUserRole
- TWorkWindowType
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Event handlers
- User scripts for the Web Verification Station
- Creating a machine-readable form
- Hot keys
- Additional options
- Description of Processing Server commands
- ABBYY FlexiCapture sample projects
- Supported recognition languages
- Supported classifier languages
- Fonts for correct characters rendering
- Supported text types
- Supported barcode types
- Supported input formats
- Processing PDF files
- Export file formats
- Date formats
- Alphabet used in regular expressions
- Patents
- Third-party technologies
- Glossary
- Technical support
- How to buy ABBYY FlexiCapture
- End-User License Agreement (EULA)
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Using scripts in ABBYY FlexiCapture
Using NLP to process unstructured documents
Natural Language Processing (NLP) is a subfield of artificial intelligence and computational linguistics. NLP is concerned with computer analysis and synthesis of natural languages. One possible practical application of NLP is the extraction of meaningful data from text.
The way a document is processed depends on its structure. For our purposes, we can distinguish three types of documents: structured, semi-structured, and unstructured documents.
- Structured documents contain a set of well defined data fields whose design, number, and placement do not change from one document to another. Examples of structured documents include forms, questionnaires, and applications.
- Semi-structured documents contain a set of data fields whose design, number, and placement can vary significantly from one document to another. They are also sometimes called "flexible documents." One example of semi-structured documents is invoices, where the number of entries and formatting often depends on the issuing company.
- Unstructured documents contain information that is not structured in any way. They also do not contain explicit data fields. Examples of unstructured documents include contracts, letters, and orders.
For more information about document types, see Types of documents processed using ABBYY FlexiCapture.
NLP technology should be used to process unstructured documents. For example, NLP can be used to extract the following types of data from a contract: reference numbers, names of parties, important dates (signing date, effective date, term, and termination date), contract price, fees, terms of payment, and so on.
To extract information from tables, structured, and semi-structured documents, other methods should be used (for example, FlexiLayouts).
Extracting information from texts
ABBYY software products use NLP models to extract information from unstructured texts. An NLP model tells the program which entities should be extracted from a document. When you train an NLP model on sample documents, the subject area of your texts and the appropriate extraction algorithm are determined so that the information you need can be extracted more efficiently. The effort required to create an NLP model depends on the variety of your documents, the context available to the program, and the complexity and amount of the information that you need to extract.
Extracting data from unstructured texts requires a lot of computing power. Larger texts will take longer to analyze.
However, the necessary information can often be found on a certain page or in a certain paragraph of a very large text. The process of finding such useful parts of text is called segmentation. This process requires considerably less time and computing resources than entity extraction, so sometimes you may want to segment a document before extracting information from it. For more information about identifying useful segments, see Creating a segmentation NLP model.
To process unstructured documents using NLP, complete the following steps:
- Install the NLP module.
- Create a Document Definition.
- Create and train an NLP model.
- Alternatively, load an existing NLP model into your Document Definition.
20.02.2021 12:32:38