Text Recognition to Intelligence - IDP with OCR Tools

Optical Character Recognition (OCR) is a digital technology that converts printed, handwritten, or scanned text into machine-readable data. Used widely in document digitization, automation, identity verification, and data entry, OCR is a key enabler for digital transformation. Multiple OCR technologies are available today, broadly speaking we can divide these in three categories - 

[1] Cloud APIs - Google Document AI, Azure Form Recognizer, Amazon Textract 

[2] On-premise  - ABBYY, LEADTOOLS, GdPicture 

[3] Open-source engines - Tesseract, PaddleOCR 

If you need to evaluate how you differentiate between these, options will depend on your use cases and long term objectives of your capture platform. Let’s go through some of the parameters on the basis of which you can assess.  

• Printed Text Accuracy

• Handwriting Recognition

• Layout and Table Parsing

• Deployment Model

• Integration Complexity

• Licensing

• Enterprise Support

In this article we will try to assess some of the OCR tools, their features and how these could potentially disrupt this space with vibe coding rise.  Let's see some leading OCR providers.

Note – This is indicative, in most cases flexible licensing models and deployments could work out between stakeholders.

There are many other OCR tools or providers, this list should contains widely know OCR providers. Let’s go through the high level details.

[1] ABBYY FineReader - Good for text recognition, accuracy is high on printed text. It’s less reliable with handwriting.  It does a good job reading layouts, identifying tables and understanding document structure. One of its standout features is how well it can automatically classify, split, and route documents. You can deploy both solutions on-premises or in the cloud. Integration is straightforward, with strong API support and connectors. ABBYY also provides enterprise-level support. 

[2] Google Document AI - Offers text recognition accuracy across a wide range of document types. It handles layout and form parsing well, making it a good option for extracting structured data from documents like invoices, receipts, and ID cards. This is a cloud-native solution. Integration is straightforward with well-documented APIs. Being part of the Google Cloud ecosystem, it also benefits from the platform’s broader AI and ML capabilities.  

[3] Azure Document Intelligence - Its capabilities are strong in layout understanding, indicating good structural interpretation. Deployment options include both cloud and container, and it offers integration ease as part of the Azure ecosystem. Its primary strength is its excellence in extracting data from structured and semi-structured forms. Provides prebuilt models for various document types e.g. receipts, invoices, IDs, and W-2 forms. Ideal for teams requiring high accuracy for printed forms and structured documents, especially those with existing Azure infrastructure.  

[4] Amazon Textract - Provides good layout and form parsing capabilities. As a cloud-based solution, integration ease is medium and it offers good support. Key strengths include specialized APIs for forms, tables, and specific document types like invoices and IDs. It can also read handwritten text. Suitable for AWS users who need integrated OCR for text, forms, and tables, with some support for handwriting.  

[5] Tesseract OCR - Offers fair accuracy on printed text and average performance on handwriting. Layout and form parsing capabilities are basic. It is an on-premise and free solution, offering medium integration ease. Support is community-driven. Its primary strengths are its open-source nature, high customizability. It can be combined with other libraries like OpenCV to enhance its performance. Consideration for open-source solutions is the hidden cost associated with the application. While "free" in terms of licensing, it needs internal development effort and ongoing maintenance to achieve enterprise grade performance.  

[6] GdPictureOCR - Has strong layout and form parsing capabilities. Primarily an on-premise solution for.NET/C++ environments, high integration ease and enterprise-grade support. Its key strengths include powerful document OCR capabilities, high-speed batch scanning, support for over 100 languages, image quality optimization, and secure document handling.  

[7] LEADTOOLS OCR SDK - It supports forms and tables. Deployment is on-premise or embedded, with medium integration ease. Enterprise-grade support is available. Its key strengths include superior OCR processing speeds, preset confidence levels, AI capabilities, and built-in or user-defined lexicons. It supports over 40 languages and can export to more than 40 different formats. SDK is suitable for developers building on-premise document imaging and recognition applications. 

[8] IronOCR (.NET) - IronOCR offers medium-high accuracy, enhancing Tesseract's capabilities with additional cleanup processes and claiming better accuracy with an accelerated engine. Its layout and form parsing are basic. It is an on-premise solution for.NET environments with high integration ease and enterprise-grade support. It incorporates advanced image pre-processing to effectively handle poorly scanned documents (noise reduction, deskewing and distortion correction). It supports multi-lingual OCR and outputs structured data. 

[9] Rossum OCR API - Recognized for its strong capabilities in invoice and data extraction due to its specialization. Its layout and form parsing are strong for invoice-specific data, focusing on automated and accurate extraction. It is a cloud-based or private deployment solution with high integration ease, offering API and SDK support. Also provides validation screens for human-in-the-loop. Ideal for businesses primarily focused on automating high-volume invoice processing and accounts payable workflows. 

[10] Nanonets OCR API - AI-driven with accuracy improving over time. It offers strong structured data extraction capabilities, integrating multimodal understanding to interpret information into Markdown or HTML. Deployment options include cloud and hybrid environments, with high integration ease through its API and SDK support. This solution is suited for businesses needing to extract complex forms and handwriting. 

With the rise in the culture of Vibe coding picking up, IDP space is evolving quickly. It is obvious to work on a strategy where teams can develop wrappers around OCR engines outputs. Vibe coding here refers to using AI and prompts to build an app or website, a trend companies and tech teams are getting into. But even as it makes coding easy as sending a prompt, it presents challenges of increased scrutiny, more reviews and requirement of handholding for engineers. Getting back to what is needed to make apps work beyond OCR engine.  

The second box above relates to creating the OCR layer, what we covered earlier. Beyond that IDP products covers much more ground like Indexing queues, structure & layout understanding, document preprocessing etc. Horizontally you need some more components for processing,

• Platform Services and Flexibility to handle documents in a pipeline in cloud setup or on-prem.  

• Security & Compliance to handle document data securely and meeting all regulatory needs. 

• Reporting, Auditing, Monitoring & Alerting to cover logging and maintainability aspects of the use cases.  

[Summary] 

OCR engines alone do not constitute an IDP platform. Enterprise-grade IDP solutions require an ecosystem that goes beyond recognition. OCR serves as an important piece, but true document intelligence comes from what happens after the text is recognized. As API-first tools grow, we might see a shift toward mini-IDP platforms. IDP platforms are challenged by open-source OCR engines (like Tesseract and others) and LLM integrations, offering low-cost alternatives. To stay relevant vendors are investing in cloud, low-code interfaces and embedding AI capabilities for workflows. In summary, OCR is just the starting point. The strength of IDP lies in systems that combine OCR with orchestration, validation, and compliance. Going forward, whether teams choose APIs, SDKs, or vibe-coded wrappers, the ability to piece together the right IDP components will open up new ways.