How Businesses Can Leverage AI-Powered OCR

AI-powered optical character recognition (OCR) once started as a simple neural network barely capable of analyzing text layout. Now it has evolved into an advanced technology that captures handwritten text with an impressive 98.5% or 98.8% accuracy.

With such capabilities, AI-powered OCR becomes crucial for the industries that involve documentation and image processing.

How do AI-based OCR tools work? And how can businesses benefit from them?

In this article, we will explore AI-powered OCR and its application for business.

Optical character recognition is a technology that converts printed or handwritten text into a machine-readable format. It can “read” and digitize text from a variety of documents, including:

• Photographs of printed materials, such as books or magazines
• Scanned documents like PDFs, images of contracts, or forms
• Handwritten notes
• Invoices and receipts
• ID cards and passports
• Websites, presentations, or app screenshots
• Street signs and product labels
• Photographs of whiteboards or chalkboards

By digitizing such text, OCR enables more efficient organization of data, allowing computers to edit, search, or process it automatically.

Optical character recognition technologies have a long history. Initially launched in the 1950s as a scanner used for converting texts from books or documents to images, it has gone through a significant evolution. Nowadays, it is almost indivisible from AI algorithms that capture, process, correct, and digitize printed or handwritten text, even from images of the lowest quality.

The shift towards AI-driven OCR began with the release of OCRopus in 2007, a Google-sponsored OCR system designed for high-volume document digitization projects. Since then, the capabilities of AI that power OCR tools have improved drastically. Modern OCR platforms apply computer vision, a branch of AI focused on interpreting and understanding visual information. Computer vision provides advanced capabilities for text recognition, context interpretation, and error correction in image and document processing.

Driven by artificial intelligence, OCR gains a new momentum, as the global market for optical character recognition is expected to reach $32.9 billion by 2030, growing at a CAGR of 14.8% from 2024 to 2030.

An important factor behind the growing popularity of OCR technologies is the rising demand for intelligent document processing (IDP) solutions that widely apply the technology to digitize documents. In fact, the global market for IDP software is projected to grow at a compound annual rate of 33.1% between 2025 and 2030, reaching an estimated $12.35 billion by 2030.

Let’s explore the workflow behind AI-based OCR, as well as its main steps. Typically, it looks as follows:

1. The solution acquires an image or a PDF document.
2. It pre-processes the image or a document by removing unwanted marks and artifacts, applies skew correction to fix tilted or misaligned text, and adapts image contrast to make the text more detectable.
3. Computer vision analyzes text layout, differentiates between printed and handwritten text, and recognizes structured data formats, such as invoices, forms, and receipts.
4. An ML model recognizes different fonts and handwriting styles and extracts text from images or PDF documents.
5. The solution fixes the extracted text with spell-checking algorithms based on natural language processing (NLP) and ensures contextual accuracy.
6. The extracted text is converted into a machine-readable format, such as JSON or XML.

AI-driven document processing can provide a significant boost to workflows across various industries in a wide range of software solutions. For example, AI OCR solutions can reduce the time required for retrieving data from handwritten, printed, and digital healthcare records from 20 minutes per file to under 5 seconds. The image below illustrates the most common software types where you can apply this technology.

By integrating AI-powered OCR into their software, organizations can enhance the workflows outlined below.

Mistakes in documentation may be extremely costly, as 22% of billing errors in healthcare result in claim denials and disputes. This applies not only to healthcare but also to other highly regulated industries such as fintech, insurtech, and legaltech.

Enhanced with a properly-tuned large language model (LLM), OCR can extract and analyze key clauses, dates, and parties from contracts, as well as critical data in other types of documents. Moreover, AI-powered OCR can even process photographs of crucial documents to ensure fast risk assessments and compliance checks.

Such functionality is implemented in Leobit’s PoC for an AI-powered image processing platform. The solution uses OCR capabilities provided by Azure AI to transform the information from images into a digital format. Used in combination with a compliance tracking solution, this tool can ensure that all the data in contracts and critical documentation is consistent.

In the medical domain, approximately 40% of a healthcare provider’s time is spent on paperwork rather than patient care. Healthcare is not the only industry where documentation takes a significant amount of time. Any domain that involves a variety of documents, bills, and invoices also involves extensive paperwork. By digitizing documents, organizations can enhance these processes.

For example, an AI-based OCR solution can greatly reduce the time teams spend manually typing information from printed documents, handwritten notes, or paper forms into computer systems or databases.

The technology automatically recognizes and converts text into digital data, which allows organizations to minimize human effort. By quickly converting various documents into searchable digital files, your company can create well-organized digital libraries.

Invoice and receipt processing

OCR solutions help businesses improve expense management with their capabilities to automatically extract critical data (e.g., amounts, dates, and vendor details) from invoices and receipts. With such OCR capabilities, companies can speed up accounts payable workflows and reduce the need for manual data entry.

We at Leobit applied the capabilities of AI-powered OCR to build a PoC for an invoice and receipt processing solution. The tool uses a custom classification model to identify the type of document to be parsed (either invoice or receipt). It applies OCR automation capabilities of Azure AI Document Intelligence to extract financial data, such as totals, line items, and taxes, from the given documents.

This invoice and receipt OCR software framework can be adapted for data extraction from virtually any type of document supported by Azure AI Document Intelligence. For example, you can use the technology for legal contracts, identity proofs, resident permits, health insurance cards, and more.

As AI technologies advance, they bring not only innovation but also new avenues for identity and document fraud. In the UK alone, synthetic identity fraud now costs financial institutions more than £300 million annually. However, while AI often plays a role in enabling these threats, it also holds the key to combating them.

OCR scanning software enhanced with machine learning algorithms can accurately extract text from financial documents, identification cards, and contracts. By analyzing subtle details and cross-checking them with standard templates or official database records, AI-powered OCR solutions validate the extracted data. They identify suspicious signs and flag potential fraud.

Document change tracking

AI-powered OCR can be used for processing image-based files to automatically detect differences between document versions. Such functionality can also be used to verify consistency across copies and enhance change tracking and management.

At Leobit, we used AI-powered OCR to build a PoC for our own document comparison solution. The tool uses Azure AI Document Intelligence for document parsing, while the DiffPlex library and custom code are used to perform comparisons. The solution highlights visual differences between document versions, provides similarity scores, and delivers analytical summaries on comparison results.

An effective way to implement OCR in your software is to integrate it with the platform that provides ready-made algorithms for text recognition and document parsing.

Below is the overview of the major AI OCR platforms.

Microsoft Azure AI Vision

It is a Microsoft-powered general-purpose image analysis platform that offers OCR capabilities through its Read API. Azure AI Vision can extract printed and handwritten text from images and PDF documents while preserving all layout details. It delivers structured JSON outputs that are suitable for indexing, classification, or search operations.

Azure AI Vision excels at capturing data from unstructured image data sources, like scanned forms, photos, or signage. This makes the platform an efficient option for automating manual data collection. In fact, we used it as a core service behind our AI-powered image recognition platform that has been mentioned in one of the preceding chapters.

Azure AI Vision uses a pay-as-you-go model. Azure offers a free tier of 5,000 transactions per month, after which usage is billed per batch of transactions (for example, approximately $1 per 1,000 OCR transactions).

Microsoft Azure AI Document Intelligence

Azure AI Document Intelligence (formerly Form Recognizer) builds on Vision’s OCR foundation, enhanced with capabilities for understanding document structure. To extract text from images or PDFs more efficiently, the service considers key document parameters, including key-value pairs, tables, and semantic relationships.

Azure AI Document Intelligence provides a range of prebuilt models for common document types. The platform also supports the development of custom models to process unique document templates.

OCR capabilities of Microsoft Azure AI Document Intelligence shine when converting complex, semi-structured documents into machine-readable data with minimal manual intervention. In addition, the service’s API-first design and integration with Azure services like Logic Apps, Syntex, and Power Automate make it very convenient for developers seeking ways to integrate the service with a software solution.

Like Azure AI Vision, this service uses a pay-as-you-go pricing model, with charges based on the number of pages processed. It also includes a free tier of 500 pages per month. The price per 1,000 pages can vary depending on several factors, such as document type, task type (e.g., classification or data extraction), deployment option (cloud, container, or disconnected environment), region, etc.

This Google-powered suite includes OCR as one of its core capabilities. The platform offers a document text detection feature powered by deep learning, enabling accurate text extraction from images and PDFs. Additionally, the API provides detailed metadata like text coordinates, confidence levels, and page structures.

Vision’s OCR capabilities can be efficiently applied for data archiving, content management, or making software more accessible. In addition, the service seamlessly integrates with BigQuery and Vertex AI, which allows businesses to enhance data analytics and AI-driven insights, respectively.

Google Cloud Vision API follows a pricing approach that is somewhat similar to that of Azure AI Vision. The service follows a pay‑as‑you‑go model where each image is one billable unit. The first 1,000 units per month are free. After that, you pay based on feature and volume with an average cost of $1.50 per 1,000 units.

Google Cloud Document AI

This service extends the Vision API with specialized document parsers that understand structure, hierarchy, and context. Google Cloud Document AI offers pre-built parsers for different types of documents, such as invoices, receipts, passports, bank statements, etc.

The service delivers structured and machine-readable data that can be directly integrated into ERP, CRM, or accounting systems. Its AutoML capabilities ensure fast deployment of custom models tailored to varying document sets.

Google Cloud Document AI uses a pay-as-you-go pricing model, with charges based on the number of pages processed. The price per 1,000 pages varies depending on multiple factors, such as the document type, the processor used (prebuilt or custom), task complexity (e.g., key-value extraction, table parsing), and region.

Amazon Rekognition

This service provides a suite of features for image and video analysis, enhanced with AI-powered text detection. Developers can integrate their solutions with the DetectText API to extract printed or handwritten text from both images and video frames.

Amazon Rekognition efficiently combines OCR with other vision features, such as facial recognition, object detection, and content moderation. In addition, it can work efficiently with other services from the AWS suite, such as Lambda, Comprehend, and Textract, allowing teams to build comprehensive solutions for image and video processing.

Amazon Rekognition follows a pay-as-you-go pricing model. The service offers a free tier period during which you can analyze 1,000 images per month for free. After that, you are charged per image depending on the API group and volume (for example, ~$0.001 per image for many detection APIs).

Amazon Textract

Amazon Textract shines when used in combination with Amazon Rekognition. This service focuses exclusively on document text extraction and layout recognition, delivering well-structured and machine-readable JSON outputs.

Amazon Textract can be integrated with your existing solution via the AnalyzeDocument API. For financial documents in particular, developers can use the AnalyzeExpense API. This ability to ensure focus on expenses makes Amazon Textract an efficient solution for fintech software development. When combined with AWS Comprehend (for NLP) or Step Functions (for orchestration), the service can be applied as a foundation for scalable, AI-powered document processing systems.

When using Amazon Textract, you pay for the number of pages processed. A basic cost per 1,000 pages/month is $1.50, but it may change depending on which features (e.g., text detection, forms, tables, queries, expense analysis) are used and which region your operations are located.

Veryfi OCR API

Veryfi offers an OCR API specifically designed for processing financial documents, such as receipts, invoices, bills, and statements. Veryfi’s machine learning–based OCR is built with HIPAA and GDPR compliance in mind, providing an efficient way to enforce documentation standards.

When using the Veryfi OCR API, you pay per document transaction rather than per page. The service has a free tier (up to 100 documents/month), after which you pay roughly $0.08 per receipt or $0.16 per invoice. Volume discounts are available for higher usage.

Developing an AI OCR solution can be a challenging task, which requires you to consider the following factors:

• Ability to handle diverse document formats
• Recognition accuracy across languages
• Document layout recognition capabilities
• ML model’s contextual understanding capabilities
• Integration of AI-powered OCR into existing workflows
• Tuning of machine learning models.

Leobit can help you handle all these challenges. We have significant expertise in AI and ML development and have released several solutions featuring AI-powered OCR capabilities. Our team is also experienced with the major cloud platforms that offer OCR features. In particular, we make extensive use of Microsoft Azure, as the Microsoft technology stack forms the core of our expertise. Thanks to our significant experience we are a Microsoft Solutions Partner for Digital and App Innovation. As an experienced AI development company, we use corporate LLM to power AI employees that facilitate diverse workflows, ranging from email automation to HR assistance.

Artificial intelligence revolutionizes OCR tools, enhancing them with capabilities for context understanding and error correction that enable more accurate text recognition even in low-quality images or PDF documents.

AI-powered OCR tools are widely used for:

• Document analysis and compliance tracking
• Document digitization and archiving
• Invoice and receipt processing
• Fraud and identity checking
• Document change tracking and management.

The key point is to find the team with the right technical expertise.

Whether you need help selecting the right optical character recognition platform for your project, developing custom AI models for OCR, or optimizing an existing recognition model, we are ready to help. With our OCR and AI development expertise, you will get the solution that extracts data from documents, improves workflow efficiency and precision, and reduces manual workload.

Contact us to discuss your needs and find out how we can help you leverage the capabilities of AI-based OCR.