Intelligent Capture | Knowledge Base | Definition
What Is Intelligent Capture?
Intelligent capture is software that can convert complex and variable document-based information into structured data. Intelligent Capture software uses different techniques to locate and extract data including pattern matching using regular expressions, definition of keyword/value pairs and location of tabular data column headers and rows.
Intelligent Capture Key Capabilities
When it comes to needs that go beyond transcription of images into text or conversion of TIFF files into editable Word documents, capabilities often associated with intelligent capture solutions should be considered. Let’s review those key capabilities: image ingestion, image processing, document classification and separation, data extraction and data quality.
Intelligent capture provides for a variety of ways in which information can be imported into the system. Communication systems integration (email, fax and network peripherals), network integration (FTP and fileshare), and hardware integration (scanners) are supported.
For documents that arrive as images (either through scanners, faxes or via mobile import), there is often an “image perfection” set of activities that are designed to manage the wide variety of quality issues typically encountered. These can include differences in the density of the images (measured in DPI or pixels), distortions of the image (stretching of the image or creases on the documents), and contrast (such as blur and lighting). Also for images, OCR is employed to convert the pictures of text into machine-readable text. Some solutions selectively perform OCR to reduce latencies typical with this process while other solutions convert the entire document into text.
Preprocessing is needed if you deal with scanned documents or pictures of documents taken with a mobile device. Delivering solid OCR data is heavily impacted by the quality of the image. Aspects like distortion, contrast, lighting, background/watermark removal, scaling correction and geometry correction are typically employed to ensure that an incoming document is optimized before OCR is applied. Intelligent Capture applies advanced image perfection functions.
This process takes and analyzes incoming documents to classify them into document types (e.g., contract, invoice or check payment, etc.) in order to support different types of workflows or to support subsequent data extraction tasks.
Document classification can employ simple rules such as locating keywords or it can implement machine learning which automatically identifies what are called “features” that distinguish one document type from another. The ability to easily train a system to output reliable document class assignments is an important capability of intelligent capture.
While many documents exist as single files (think PDF), many times multiple documents are stored together. Document separation is the process of identifying these “document boundaries” such that a PDF with many different documents, can be “burst” into multiple different documents, tagged, and then go through potentially different workflows. Document separation can make use of simple rules or it can implement machine learning to identify the most reliable features that indicate first, middle and last pages of a document.
Data Location and Extraction
Here we are not talking about conversion of an image of a document into text. Instead, data extraction satisfies the business need to turn documents into structured tag-value pairs that can be used by various systems. Intelligent capture offers a broader set of capabilities from the simplest ability to locate data by supplying field-level X-Y coordinates to more sophisticated location techniques such as presence of keywords, relative proximity of one data element to another or pattern matching so that the right data is accurately extracted.
This step involves both automated and manual processes to ensure the data output from an intelligent capture solution is accurate. Automated methods can make use of user-supplied information such as dictionaries or integrations into third-party data stores, all the way to more-complex capabilities such as statistical analysis of output to score reliability of data at a field level. Manual validation includes workflows and special user interfaces that route suspect output to specific staff who review, approve or make corrections prior to exporting the data to another system.