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Mike Smart

Mike is a Senior Analyst and Operations Officer at NelsonHall. His main research focus is on digital transformation technologies, including RPA, blockchain, IoT, artificial intelligence, cognitive, and machine learning.

LTI Mosaic Entity Extractor - Document Cognition SmartLabTest

Vendor Analysis

by Mike Smart

published on Apr 09, 2020

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Report Overview:

NelsonHall’s LTI Mosaic Entity Extractor document cognition benchmark report is a functional “lab” test evaluation of the Entity Extractor document cognition platform.

Who is this Report for:

NelsonHall’s LTI Mosaic Entity Extractor document cognition benchmark report is a functional “lab” test evaluation of the Entity Extractor document cognition platform, designed for:

  • Sourcing managers monitoring the capabilities of existing suppliers of Document Cognition platforms and identifying vendor suitability for document cognition RFPs
  • RPA and intelligent automation center of excellence personnel evaluating document cognition platform capability
  • Vendor marketing, sales, and business managers looking to benchmark their platforms against their peers
  • Financial analysts and investors covering intelligent automation and Document Cognition platforms.

Scope of this Report:

The report provides a comprehensive and objective analysis of LTI Mosaic Entity Extractor’s capabilities, including:

  • Designing the document cognition models
  • Document Ingestion
  • Document verification
  • Testing.

This report provides the quantitative results of the SmartLabTest comparing the platform’s performance for each document type. This report includes:

  • The SmartLabTest results
  • Analysis of the platform’s strengths & weaknesses
  • Identification of the key features of each platform
  • An evaluation of comparative platform maturity

Key Findings & Highlights:

This NelsonHall vendor assessment tested LTI Mosaic Entity Extractor’s capabilities in ingesting and interpreting:

  • Structured documents such as mortgage applications and ACORD filings
  • Semi-structured documents such as invoices and purchase orders
  • Highly unstructured documents such as resumes.

The KPIs assessed for each document type included:

  • Proportion of fields correctly recognized
  • Accuracy of extraction of recognized fields
  • Proportion of fields overall that 100% accurate and require no manual intervention.

Entity Extractor leverages AI/deep learning and uses one of three methods to extract data: document-based extraction, which best extracts data from PDF and DOC files; image-based extraction which can extracts data from JPG and PNG files; and image zoning based extraction for complex images which need to be zoned before OCR to analyze structured files which contain images. 

Ingested documents are processed through a number of processing steps; ingested, object identification, text extraction in progress, ready for verification, QC in progress, and QC complete.

Mosaic Entity Extractor’s verification station shows the original document, the raw text which has been extracted, and the extracted data by label. Data within the converted document are highlighted by color-coding that matches a colored label at the top right of the field on the fields list, helping users with the traceability and QC of data. 

The configuration of Entity Extractor is generally provided by LTI. 

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