Three Approaches to Data Extraction

for ease of access, refer to the table below titled ‘OCR, ML OCR, IDP comparison’

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Text Extraction
Basic extraction of text
Advanced text extraction
Advanced text extraction
Document Complexity
Limited to simple formats
Handles complex formats
Handles complex formats
Language Support
Layout Flexibility
Limited adaptability
Adaptive to various layouts
Adaptive to various layouts
Basic/manual processing
Automation capabilities
Advanced automation capabilities
Limited integration options
Seamless integration options
Seamless integration options
Cognitive Capabilities
Limited cognitive abilities
Advanced cognitive abilities
Additional Features
Intelligent insights
Additional AI-driven features

At Infrrd, we understand that your document processing needs are unique

That’s why we’re here to guide you towards the perfect solution that meets your goals.

Dig deeper with our comprehensive guide and make an informed decision.
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Effortlessly extract text with unparalleled precision and lightning-fast speed. With ML OCR, you’ll streamline workflows, boost accuracy, and dominate the processing of diverse document types. From invoices to contracts, ML OCR’s cutting-edge machine learning algorithms adapt flawlessly to any layout, font, or language. Experience the transformativ e power of ML OCR and unlock a new era of accurate text extraction.



Unleash the full potential of document processing with the comprehensive power of IDP. Automate your end-to-end workflows seamlessly, from text extraction and classification to data validation and intelligent decision-making. Handle even the most complex document types with ease - whether it’s invoices, purchase orders, or compliance documents. Harness the combined strength of OCR, machine learning, and NLP to achieve unparalleled data accuracy, streamline processes, and propel your business to new heights with IDP.

Intelligent Document Processing

Navigating the Landscape: Comparing ML OCR and IDP - Scalability, Functionality, and Manageability



  • May have limitations in scalability as it primarily focuses on text extraction
  • May be suitable for businesses with moderate document processing needs and smaller document volumes
  • Scaling ML OCR to handle larger document volumes may require additional resources or infrastructure investment


  • IDP offers robust scalability to handle large volumes of documents and complex processing workflows
  • It is designed to accommodate growing business needs and can efficiently process increasing document volumes
  • IDP provides the flexibility to scale up or down based on the organization’s requirements, ensuring optimal performance and resource utilization




  • ML OCR specializes in accurate and efficient text extraction from various document types
  • It excels in its ability to handle diverse layouts, fonts, and languages, adapting to different document structures
  • Leverages advanced machine learning algorithms, continually improving accuracy over time


  • IDP extends beyond text extraction, offering a comprehensive set of functionalities
  • It includes features such as document classification, data validation, entity recognition, and intelligent decision-making
  • IDP enables end-to-end automation of document-based processes, providing a robust solution for complex workflows




  • ML OCR generally offers easier management and maintenance due to its narrower focus on text extraction
  • Implementation and configuration processes are typically simpler and more streamlined
  • ML OCR provides a user-friendly solution with a shorter learning curve, making it easier for users to adapt and operate


  • IDP may require more robust management due to its broader range of capabilities and integration with complex workflows
  • It offers advanced configuration options to tailor the solution to specific business needs
  • While IDP may have a slightly steeper learning curve, it provides extensive flexibility and customization capabilities to meet unique requirements


FAQs on Machine Learning OCR

How is an AI-based OCR solution used in real life?

In real-life applications, an AI-based OCR solution is extensively employed by over 60% of major corporations worldwide to streamline data input across various stages of business processes. Utilizing OCR technology enables businesses to seamlessly integrate, convert, and connect to diverse platforms through email, fax, or traditional EDI.

Additionally, the system automates the storage of data, enhancing efficiency and accessibility. The incorporation of machine learning in OCR further refines accuracy and adaptability in recognizing and interpreting textual content. For a comprehensive guide on optimizing OCR processes, explore the insights in the ebook titled "Say Goodbye To OCR Blues And Burnout."

What type of technology is OCR?

OCR (Optical Character Recognition) is primarily powered by computer vision and machine learning (ML) technologies, with an infusion of artificial intelligence (AI). This advanced technology automates the extraction of data from printed or typed text within scanned documents or image files.

Through OCR, the extracted text is converted into a machine-readable format, enabling seamless integration into data processing tasks such as searching or editing. The incorporation of AI in OCR enhances its capabilities, allowing for more accurate and adaptive recognition of characters and patterns.

AI OCR, at the intersection of artificial intelligence and optical character recognition, represents a sophisticated solution for automated data extraction and processing.

To know more, book a 15-min session with an IDP expert

What are OCR AI capabilities?

OCR (Optical Character Recognition), also referred to as text recognition or text extraction, harnesses the capabilities of machine learning. Utilizing machine learning OCR methods, this technology enables the reading of printed or handwritten text from a diverse range of documents, including papers, reports, applications, and invoices.

Additionally, it extends its functionality to extract text from images, encompassing sources such as posters, street signs, and product labels. The integration of OCR with machine learning enhances its adaptability, making it a powerful tool for accurately recognizing and extracting text from various visual sources.

To know more, book a 15-min session with an IDP expert

How to choose OCR machine learning software?

When choosing OCR machine learning software, prioritize the following:

Comprehensive Data Processing: Ensure the software can extract and process data from various sources.

Textual Interpretation: Look for high-level textual interpretation capabilities for accurate content comprehension.

Intuitive User Interface: Opt for software with a user-friendly interface for efficient operation.

Efficient Data Cleaning: Select software with AI-driven capabilities for effective data cleaning and organization, ensuring accuracy and consistency.

Feel free to schedule a demo to speak to one of our experts at Infrrd.

How does OCR work with machine learning?

OCR (Optical Character Recognition) works with machine learning by employing algorithms that learn patterns and features from visual data. In machine learning OCR systems, the algorithms are trained on diverse datasets to recognize characters and structures within images or documents. This training allows the OCR system to adapt and improve its accuracy over time, enhancing its ability to accurately extract and interpret text from various sources.

To know more, book a 15-min session with an IDP expert

What are the key challenges in implementing machine learning OCR systems?

Implementing machine learning OCR systems presents challenges related to the diversity of document types. These systems need to be trained on expansive datasets that encompass various document structures and layouts.

Handling variations in fonts and adapting to evolving document structures pose additional challenges. Moreover, ensuring robustness against errors introduced by image noise or poor-quality images is crucial. The continuous evolution of machine learning models to overcome these challenges is essential for maintaining high accuracy in OCR results.

Schedule a demo with one of our experts to learn more.

How can I improve the accuracy of OCR results in machine learning systems?

To bolster the accuracy of OCR results in machine learning systems, it's vital to curate training datasets that represent the diversity of documents the system will encounter. Fine-tuning machine learning models to accommodate variations in fonts, layouts, and structures enhances adaptability.

Image preprocessing techniques, such as noise reduction and image enhancement, contribute to improved image quality, subsequently boosting OCR accuracy. Implementing post-processing methods, including error correction algorithms, serves as a final step in refining results and ensuring optimal accuracy in machine learning OCR systems.

To know more, book a 15-min session with an IDP expert

What types of documents can be processed using machine learning OCR?

Machine learning OCR systems exhibit remarkable versatility, adept at processing an extensive array of documents. From traditional papers and reports to intricate applications, invoices, eye-catching posters, street signs, and product labels, the adaptability of machine learning OCR, infused with artificial intelligence, empowers it to seamlessly handle diverse document types.

This flexibility makes machine learning OCR a robust and versatile solution for extracting text from an extensive range of visual sources with precision and efficiency.

To know more, book a 15-min session with an IDP expert

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