Why Do LLMs Fail at OCR and Document Parsing?

Why Do LLMs Generate Inaccurate OCR Results?

LLMs are designed to generate text based on probabilities, which makes them prone to hallucinations—creating content that was never present in the source document. This is a significant issue when performing OCR, as even minor errors can result in lost or misinterpreted data. Unlike purpose-built solutions that use result confidence scoring to validate accuracy, LLMs lack the precision required for reliable text extraction.

When working with financial documents or identity documents, accuracy is paramount. A single misread character in an invoice or MICR cheque can lead to significant financial discrepancies.

How Do LLMs Struggle with Document Structure?

Unlike dedicated OCR tools, LLMs struggle to extract structured data from documents, making them unsuitable for parsing invoices, forms, and other structured documents accurately. Specialized OCR solutions offer features like table extraction and region-specific OCR, allowing precise extraction of data from specific areas of a document. LLMs cannot reliably identify and maintain document structure, particularly when dealing with multi-column layouts or complex forms.

What Makes LLM OCR Computationally Expensive?

Running OCR with an LLM typically requires substantial computational resources, as the models must process large amounts of text data before generating meaningful output. This results in higher costs and slower performance compared to optimized OCR solutions. In contrast, dedicated OCR libraries offer fast configuration options and multithreading support for efficient processing.

For enterprise applications processing thousands of documents, the computational overhead of LLMs becomes prohibitive. Solutions like IronOCR can leverage async processing and abort tokens for better resource management.

When Do LLMs Fail with Different Document Types?

LLMs may work reasonably well for simple text documents but often struggle with scanned PDFs, handwritten text, or documents with complex formatting. Their performance varies widely depending on the document type, making them unreliable for enterprise applications. Specialized OCR tools excel at handling diverse document types, including:

• Handwritten images
• License plates
• Passports
• Screenshots
• Multi-page TIFF files

Why Can't You Control AI OCR Output?

AI models operate as black boxes with predetermined processing pipelines, leaving users unable to adjust parameters for specific document types or quality requirements. In contrast, dedicated OCR solutions offer extensive customization options:

• Image DPI settings for optimizing resolution
• Color correction filters for improving contrast
• Orientation detection for automated rotation
• Noise reduction filters for cleaner extraction

What Privacy Risks Exist with AI-Based OCR?

Uploading documents to external AI services means your sensitive data travels over the internet and may be stored on third-party servers, creating potential security vulnerabilities. When processing passports, financial statements, or MICR cheques, data privacy is critical. Local OCR solutions ensure complete control over your data.

How Does AI OCR Limit Integration Options?

AI chatbots provide text in conversational format rather than structured data, making it difficult to integrate results into automated workflows or existing applications. Professional OCR tools offer multiple output formats:

• Searchable PDFs
• hOCR HTML export
• Structured data output
• Stream processing

How Does IronOCR Achieve Higher Accuracy Than LLMs?

IronOCR is optimized for extracting text from images and PDFs with precision. Unlike LLMs, it doesn't generate hallucinated text but rather extracts exactly what's present in the document. The library uses Tesseract 5 with advanced computer vision capabilities to ensure accurate results. Additionally, IronOCR provides confidence scores for each extracted element, allowing developers to validate results programmatically.

Why Is IronOCR Better for Business Documents?

IronOCR can accurately process structured documents such as invoices, contracts, and forms, making it ideal for businesses that rely on precise data extraction. The library includes specialized methods for:

• Reading tables in documents
• Extracting data from specific regions
• Processing multi-page documents
• Handling various barcode formats

What Makes IronOCR More Cost-Effective?

Unlike LLM-based OCR, which requires significant computational power, IronOCR is lightweight and optimized for speed. This makes it a cost-effective solution that doesn't require expensive cloud-based models. The library offers:

• Fast OCR configuration
• Multithreading support
• Local processing without API costs
• One-line OCR implementation

How Does IronOCR Handle Poor Quality Scans?

IronOCR includes built-in noise reduction and image enhancement capabilities, allowing it to extract text from noisy, low-resolution, or distorted scans more effectively than LLMs. The library features:

• Image optimization filters
• Fix image orientation
• DPI enhancement
• Color correction
• Filter wizard for automatic optimization

What Are the Key Features of IronOCR?

IronOCR offers a range of features that make it an industry-leading OCR solution:

• Multi-Language Support: Recognizes text in 125 international languages
• Advanced Document Capabilities: Handles passports and license plates
• PDF and Image OCR: Works with PDFs, TIFFs, JPEGs, and other formats
• Searchable PDFs: Converts documents into searchable PDFs
• Barcode Recognition: Detects over 20 barcode formats

Which Document Types Does IronOCR Support?

IronOCR handles various document formats including PDFs, images (JPEG, PNG, TIFF), and specialized documents like passports and license plates. The library also supports:

• System.Drawing objects
• Stream processing
• Multi-frame TIFFs and GIFs
• Photo OCR

How Does IronOCR Enable Multi-Language Recognition?

IronOCR supports over 125 languages and can detect multiple languages within a single document, making it ideal for international applications. The library allows:

• Custom language file usage
• Multiple languages in one document
• Custom font training

How Does IronOCR Extract Text from Images?

using IronOcr;

class Program
{
    static void Main(string[] args)
    {
        // Specify the path to the image file
        string imagePath = "example.png";

        // Initialize the IronTesseract OCR engine
        var Ocr = new IronTesseract();

        // Create an OCR image input from the specified image path
        using var imageInput = new OcrInput(imagePath);

        // Perform OCR to read text from the image input
        OcrResult result = Ocr.Read(imageInput);

        // Output the recognized text to the console
        Console.WriteLine(result.Text);
    }
}

using IronOcr;

class Program
{
    static void Main(string[] args)
    {
        // Specify the path to the image file
        string imagePath = "example.png";

        // Initialize the IronTesseract OCR engine
        var Ocr = new IronTesseract();

        // Create an OCR image input from the specified image path
        using var imageInput = new OcrInput(imagePath);

        // Perform OCR to read text from the image input
        OcrResult result = Ocr.Read(imageInput);

        // Output the recognized text to the console
        Console.WriteLine(result.Text);
    }
}

What Happens When Using LLMs for OCR Tasks?

For this example, we've followed the steps outlined below to have Google's LLM, Gemini, perform OCR on the same image.

Steps for Performing OCR with Google Gemini

Why Is IronOCR More Practical for Developers?

One major limitation of AI-powered OCR is that the extracted text is simply presented in a message, making it difficult to use for further processing. With IronOCR, the extracted text can be directly used in .NET applications for automation, search indexing, data processing, and more. The library provides:

• Structured result objects with detailed metadata
• Export to various formats including searchable PDFs
• Image export capabilities for debugging
• Highlighting text for debugging

This allows developers to seamlessly integrate OCR results into their workflows without manually copying and pasting text from an AI chatbot.

Why Choose IronOCR Over Google Cloud Vision API?

IronOCR provides a superior experience for .NET developers compared to Google Cloud Vision API for several reasons:

1. No External API Calls
   Google Cloud Vision requires internet access and authentication. IronOCR runs locally, eliminating latency, security concerns, and service dependencies.

2. Simpler Setup
   Google Cloud Vision requires credentials and API key management. IronOCR works with a simple NuGet package installation.

3. Better .NET Integration
   IronOCR is built specifically for .NET, providing seamless integration across all platforms.

4. More Control Over OCR Processing
   IronOCR allows extensive customization through filters and configuration. Google Cloud Vision is a black-box solution.

5. Lower Cost for On-Premises Use
   Google Cloud Vision charges per request. IronOCR has a one-time license, more cost-effective for large-scale applications.

When Should You Use Local OCR Over Cloud Services?

Local OCR solutions like IronOCR are ideal when you need data privacy, offline capability, or predictable costs without per-request pricing. They're particularly valuable for:

• Processing sensitive financial documents
• Working with identity documents
• High-volume batch processing
• MAUI applications

What Security Benefits Does IronOCR Provide?

Running OCR locally means sensitive documents never leave your infrastructure, ensuring compliance with data protection regulations and eliminating third-party access risks. IronOCR provides:

• Complete data isolation
• No internet dependency
• Security CVE monitoring
• Enterprise-grade licensing options