USING IRONOCR

Optimized Performance for Faster, More Efficient OCR Processing

In 2024.12, IronOCR introduced a feature that significantly reduced the file size of generated searchable PDFs when processing multi-page TIFF images. While this improvement achieved smaller output files, it also introduced performance challenges in processing speed and memory usage.

Initial optimizations in 2025.1 began addressing these performance issues. The comprehensive performance improvements were later delivered in the 2025.2 release, which maintained the smaller file sizes while resolving the speed and memory challenges when handling multi-page documents.


Identifying the Bottleneck: Page Rotation & Processing Time

One major performance bottleneck was page rotation. Each operation created a new instance instead of reusing existing ones, leading to unnecessary processing time and memory consumption. This inefficiency resulted in increased processing time and memory consumption, particularly when converting large TIFFs into searchable PDFs.

Optimization Version (IronOCR 2025.2)

The initial fix in IronOCR 2025.1 focused on optimizing instance reuse for page rotation. This led to a modest 10% improvement, reducing processing time from 63 seconds to 57 seconds. But this was just the beginning.

As we delved deeper, we identified several other areas for enhancement.


Systematic Performance Enhancements

Key Areas of Improvement

  • Instance Reuse for Repeated Operations
  • Optimizing Concurrent Processing
  • Memory Allocation & Object Lifecycle Management
  • Searchable PDF Rendering Improvements

Each of these optimizations built upon the last, leading to the breakthrough improvements in IronOcr 2025.2.


Key Observations: What Changed?

With these enhancements, the IronOcr 2025.2 update delivered significant performance improvements:

Faster Processing:

  • 24-page searchable PDFs now process 49% faster (63s → 32s).
  • Multi-document processing improved by 45%.
  • Single image OCR is 35% faster.

More Efficient Memory Usage:

  • Object allocations reduced by up to 63%.
  • Less memory fragmentation led to smoother performance.

Benchmark Results

ReadSimpleImage:

  • 2025.2 version: 867.1 ms, 16.27% faster than 2024.11 version, 34.83% faster than 2025.1 version.

Optimized OCR Processing 1

  • Memory: 81.65 MB, 31.27% less than 2024.11 version, 42.12% less than 2024.12 version.

Optimized OCR Processing 2

ReadMultipleDocs:

Optimized version (2025.2): 20706.6 ms, 15.61% faster than (2024.11) version.

Optimized OCR Processing 3

  • Memory: 1.2 GB, 4.76% less than Legacy.
  • Pdfium version: Failed the benchmark

Optimized OCR Processing 4

Real-World Applications: How Businesses Benefit

A law firm digitizing legal contracts previously faced slow OCR processing when handling multi-page scanned agreements. With IronOcr 2025.2, they can now convert contracts into searchable PDFs nearly 50% faster, streamlining case research and compliance checks.

Healthcare: Efficient Medical Record Processing

Hospitals and clinics often deal with large TIFF scans of patient records. Before, converting a 24-page medical history document into a searchable PDF took over a minute. Now, with improved memory management and concurrent processing, this task is completed in just 32 seconds, allowing for faster access to critical patient data.

Finance & Auditing: Handling Bulk Reports

Accounting firms scanning hundreds of pages of financial reports needed a solution to keep file sizes manageable while ensuring text remained searchable. With IronOCR’s refined rendering, they can now process multi-document scans more efficiently, reducing both processing time and final file sizes.

Research & Archives: Preserving Historical Documents

Archivists working with scanned research papers and historical documents require highly accurate text recognition while keeping files lightweight for storage. The latest optimizations allow them to handle large-scale document conversions with significantly lower processing overhead.


The Evolution of Searchable PDFs: A Process, Not Just a Jump

Optimization isn’t a single leap forward, it’s a step-by-step process shaped by real-world challenges.

  1. 2024.11: Introduced file size reduction for searchable PDFs but encountered performance limitations.
  2. 2024.12: Rendering improvements reduced PDF file sizes but revealed speed and memory issues with large TIFFs.
  3. 2025.1: Addressed the first bottleneck in page rotation processing, improving processing time by 10%.
  4. 2025.2: Comprehensive optimizations delivered a 49% performance boost, improved memory efficiency, and smoother handling of large searchable PDFs.

Each update builds upon the lessons from the last, resulting in an OCR engine that’s faster, more efficient, and ready for high-demand workloads.


Experience the Power of the Latest IronOcr Update

If your business relies on fast, efficient, and accurate OCR processing, this IronOCR 2025.2 update delivers the speed and optimization you need.

Try Free Trial Key for 30 days and experience the process! 🚀

Frequently Asked Questions

What are the key optimizations in the latest OCR software update?

IronOCR 2025.2 includes key optimizations such as instance reuse, concurrent processing, improved memory management, and enhanced PDF rendering, resulting in faster processing and reduced memory usage.

How much faster is OCR processing with the recent update compared to previous versions?

OCR processing is up to 49% faster in IronOCR 2025.2 compared to previous versions, with improvements like 35% faster single image OCR and 45% faster multi-document processing.

How does the updated software improve memory usage?

IronOCR 2025.2 improves memory usage by reducing object allocations by up to 63% and minimizing memory fragmentation, leading to more efficient performance.

What performance issues were addressed in the recent OCR updates?

IronOCR 2025.1 began addressing performance issues related to page rotation inefficiencies. IronOCR 2025.2 further resolved these issues and others, significantly improving processing speed and memory usage.

Why is page rotation a performance bottleneck in OCR processing?

Page rotation was a performance bottleneck because each operation created a new instance, increasing processing time and memory consumption. Optimizing instance reuse helped mitigate this issue.

How does the latest OCR update benefit businesses in legal and compliance sectors?

IronOCR 2025.2 allows law firms to digitize legal contracts and convert them into searchable PDFs nearly 50% faster, enhancing case research and compliance checks.

What advantages does the recent OCR update offer the healthcare industry?

In healthcare, IronOCR 2025.2 improves the processing of large TIFF scans of patient records, enabling faster access to critical data by completing tasks in just 32 seconds compared to over a minute previously.

How does the updated OCR software impact finance and auditing processes?

IronOCR 2025.2 helps accounting firms process multi-document scans more efficiently, maintaining manageable file sizes while ensuring text remains searchable, thereby reducing processing time.

What improvements were made in the image reading function in the latest OCR update?

The ReadSimpleImage function in IronOCR 2025.2 is 16.27% faster than the 2024.11 version and 34.83% faster than the 2025.1 version, with memory usage reduced by 31.27% and 42.12% respectively.

How does the recent OCR update enhance document preservation for research and archives?

IronOCR 2025.2 allows archivists to handle large-scale document conversions with lower processing overhead, preserving historical documents with accurate text recognition while keeping files lightweight for storage.

Kannaopat Udonpant
Software Engineer
Before becoming a Software Engineer, Kannapat completed a Environmental Resources PhD from Hokkaido University in Japan. While pursuing his degree, Kannapat also became a member of the Vehicle Robotics Laboratory, which is part of the Department of Bioproduction Engineering. In 2022, he leveraged his C# skills to join Iron Software's engineering team, where he focuses on IronPDF. Kannapat values his job because he learns directly from the developer who writes most of the code used in IronPDF. In addition to peer learning, Kannapat enjoys the social aspect of working at Iron Software. When he's not writing code or documentation, Kannapat can usually be found gaming on his PS5 or rewatching The Last of Us.
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