ML OCR-min copy

Machine Learning OCR

Processing unstructured, complex documents and images

Add machine learning to OCR and what do you get?  A better OCR. 

It's kind of like buying a faster horse in the age of the automobile.

In the age of AI, maybe it's time to test drive IDP. 

Intelligent Data Processing (IDP)

IDP uses a full stack of powerful AI technologies to automate the processing of data from your most challenging complex documents.

Three Approaches to Data Extraction



OCR is a mature extraction technology that works for simple use cases.

What to know about OCR

  • OCR systems have limitations.

  • It is not able to process unstructured, complex documents.

  • OCR templates create management overhead and do not scale.
Machine Learning OCR

ML OCR ⭐ ⭐ ⭐

ML OCR attempts to correct OCR's limitations with machine learning. 

What to know about ML OCR

  • Machine Learning OCR uses AI technology reduce some of OCR’s shortcoming.

  • ML is used to help preprocess documents so the OCR can handle more complexity.

  • But templates are still used, and it remains limited in the document complexity it can handle.
Intelligent Data Processing

IDP ⭐ ⭐ ⭐ ⭐ ⭐

IDP is a different approach that is not OCR-based.

What to know about IDP

  • Advanced and proprietary AI techniques are used to unlock data from the most unstructured, complex documents.

  • It does not use templates.

  • IDP delivers better performance than OCR and ML OCR approaches.

Do you know the 5 OCR risks?

Download the OCR infographic
Get the Infographic: IDP Vs OCR

Use Cases and Document Types

OCR and ML OCR assumes documents are structured and consistent

IDP processes

IDP assumes documents will change and can process complexity, unstructured layouts and noisy documents.


Unstructured Documents

  • Layouts that change over time or between sources.
  • Data that is not always in the same location.
  • Long PDFs made of multiple document types.

Complex Documents

  • Complex tables
  • Stamps, logos, symbols
  • Graphs, charts, images
  • Handwriting

Noisy Documents

  • Noisy, has colors, low quality
  • Inconsistent orientation
  • Data with contextual relationships
  • And more

Dig deeper with the Executive Guide to IDP

Download the Executive's Guide to IDP


OCR and ML OCR use templates to tell the OCR where to extract the data. Templates are notoriously hard to scale and requires significant maintenance.

IDP is template-free. The system understands what to extract based on AI processing. Instead of coded templates, IDP learns what and where to extract. And IDP learns overtime and improves its performance without need to template modifications.

✔️ IDP wins on scalability

scalability - IDP processes


IDP is built on an AI Platform that provides more functionality than just converting an image into text that an OCR does.

IDP's functionality includes:

  • high-accuracy extraction
  • document classification to any level
  • data cleaning
  • data validation
  • context preservation
  • predicative insights
  • anomaly detection
  • insights generation
  • and more

✔️ IDP wins on functionality

Functionality - IDP processes


IDP includes a customer control center through which the IDP application is managed. A business analyst can add/change documents and extraction points and manage application performance.

OCR and ML OCR use templates which can be difficult to manage.

✔️ IDP wins on manageability 

Manageability - IDP processes

Let us show you how IDP goes beyond ML OCR

Schedule a guided demo today.

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