USING IRONOCR

Optimized Performance for Faster, More Efficient OCR Processing

Published February 19, 2025
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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 2024.11ver, 34.83% faster than 2025.1 version.

Optimized Ocr Processing 1 related to Benchmark Results

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

Optimized Ocr Processing 2 related to Benchmark Results

ReadMultipleDocs:

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

Optimized Ocr Processing 3 related to Benchmark Results

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

Optimized Ocr Processing 4 related to Benchmark Results

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! 🚀

Kannaopat Udonpant

Kannapat Udonpant

Software Engineer

 LinkedIn

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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