Article

UiPath Document Understanding: A Game Changer?

This article from The Silicon Partners (TSP) explains intelligent document processing and how UiPath Document Understanding works: taxonomy, OCR digitization, classification, extraction and export, using pre-trained, retrainable or custom machine learning models. It is for automation teams processing invoices, IDs and forms. TSP holds UiPath Services Network certification for Document Understanding.

Blank paper sheets lifting from a loose stack and folding into a neat row of identical cards.

Companies around the world are realizing the need for automating digital tasks through Robotic Process Automation (RPA)faster than expected. The global robotic process automation market was valued at USD 7.11 billion in 2021 and is projected to grow at an impressive rate of 23.4% annually in the current decade. An important RPA use case, andone of the fastest growing is Intelligent Document Processing (IDP).

What is Intelligent Document Processing

Intelligent Document Processing is the capability of collecting, generating, and/or interpreting documents, which allows organizations to free up resources from the manual and time-consuming work of processing digital documents manually. Intelligent Document Processing allows Bots to analyze documents and take actions based on their understanding of the documents.

There is an inherent challenge with Document Processing – as much as 90 percent of enterprise documents tend to be unstructured. To tackle this challenge, RPA solution providers empower their solutions with cutting-edge AI and Machine Learning technologies like Natural Language Processing and Deep Learning through neural networks.

UiPath Document Understanding product logo with its robot mascot.

UiPath’s Document Understanding

UiPath’s Document Understanding solution is an AI-driven technology that is developed to help read and process documents automatically with the help of software robots. Enabling AI technology makes the process intelligent because of which robots are able to identify and locate data even if the type or format of the document changes.

The solution follows a similar logical flow as the manual processing of documents –

  1. Defining and loading taxonomy

    This defines the structure and the fields that we need to extract in all the different types of documents that the process expects

  2. Digitize

    Converting the input image/pdf file into a digital format using an Optical Character Recognition (OCR) engine

  3. Classify

    Classify the input file into one of the types defined in the Taxonomy

  4. Extract

    The data from the input document

  5. Validate (optional)

    If needed, a person checks or corrects the results, and the document goes back to extraction.

  6. Export

    Finally the document is exported for further use.

Source: UiPath

UiPath encourages the use of Machine Learning Models for the Classify and Extract stages. UiPath has allowed the use of ML Models in 3 different ways –

  1. Using pre-trained ML models for frequent documents like invoices, passports, IDs, bills, purchase orders, etc. which have been trained on thousands of documents from different geographies and which can be used directly in the RPA process.
  2. Using ML models by training them on user-provided documents. These help the models learn specific use cases.
  3. Uploading and using custom ML models created for hyper-specific use cases. UiPath provides assistance in creating these custom ML models as well.

Advantages

  • Faster Processing

    A large U.S. members-only wholesale retailer has leveraged Document Understanding for processing invoices, and it claims that the invoices are now processed in 30 seconds which earlier used to take 3-5 minutes. Learn More

  • Free-up Resources

    Evros Technology group which used Document Understanding to process purchase invoices claims that it has achieved an 80% improvement in time savings, which resources use for more productive work.Learn More

  • Get Better Insights

    Chevron leverages Document Processing to extract critical data from drilling and completion reports, which makes it available for analysis almost immediately through a Microsoft Power BI Dashboard. Learn More

  • Easy Set-up

    Fisher Scientific’s Document Understanding Process for automated payment processing took around 12 weeks to set up. Learn More

  • Cost-Savings

    An AI-enabled Document Understanding process that is helping a clinic process patient data to predict high-risk pregnancy rates and enroll patients to appropriate programs, is saving the clinic $11 million annually.

UiPath Innovations around Document Understanding

Get Started with the Experts

TSP is certified in UiPath Document Understanding through the UiPath Services Network (USN). In the past 2 years, we have helped several clients achieve their automation objectives through Document Understanding. We have helped our clients in automating their payment processes, redact sensitive information for compliance purposes, and extract information from various forms and IDs for meeting company policy requirements.

In this course, we have experimented with multiple OCRs and also successfully delivered projects using the Digitization Service. We now have a good handle on multiple pre-trained Machine Learning Models and have also used the public classification endpoint for a time-sensitive project.

TSP is committed to helping our customers envision a fully automated enterprise and reach their automation goals by providing them with the latest UiPath Innovations and automation expertise.

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About the author

  • Amit Tiwari

    SVP Agentic AI, Intelligent Automation & Innovation

    Technology leader with 24+ years of experience driving enterprise transformation through Agentic AI, Intelligent Automation, SAP, AI/ML, and Digital Modernization. Focuses on shaping the Agentic Enterprise, orchestrating AI Agents, automation, enterprise systems, and people to accelerate transformation, improve productivity, reduce operational risk, and deliver sustainable ROI. Experience spans Agentic AI, AI/ML, RPA and Intelligent Automation, SAP transformation, Test Automation, Process Orchestration, Cloud, and enterprise application modernization. I am particularly interested in the convergence of AI Agents + RAG + MCP + Agent-to-Agent (A2A) collaboration + APIs + Automation + Human-in-the-Loop, and how these capabilities can transform complex enterprise processes.

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