Release Notes for MAUI SDK - 11.x

11.2.3000 (11/20/2025)

🎉Milestone Release

Version 11.2.3000 introduces a series of AI-driven improvements designed to enhance barcode detection accuracy, processing speed, and configuration flexibility.

This release focuses on practical performance gains for production environments across retail, logistics, and manufacturing workflows.

✨ Key Highlights

AI-Powered Barcode Detection and Decoding

• New Localization Models – Introduces OneDLocalization and DataMatrixQRCodeLocalization neural network models for improved detection of blurred / low-resolution 1D codes, or partially damaged DataMatrix/QR codes.
• Specialized Decoders – Adds EAN13Decoder and Code128Decoder models optimized for long-distance and motion-blurred decoding scenarios.
• Redesigned Deblur Model – The OneDDeblur model now provides more effective recovery from motion and focus blur.
• Configurable Model Selection – The new ModelNameArray parameter supports flexible model loading and fine-grained control for specific barcode types.

Precision and Processing Control

• Enhanced Deblur Methods – DM_DEEP_ANALYSIS now includes sub-level control with OneDGeneral, TwoDGeneral, and EAN13Enhanced options.
• Barcode Count Expectation – The new ExpectedBarcodesCount parameter enables format-specific quantity control and early termination in fixed-count workflows.
• Improved Region Detection – The new RPM_GRAY_CONSISTENCY mode provides more precise region extraction based on grayscale uniformity and local consistency for document and label processing.

Performance Highlights

Barcode Workflows

• Up to 26.5% higher read rates under blur conditions with as much as 44% faster processing.
• Reliable decoding of DataMatrix and QR codes with missing or damaged finder patterns.
• Extended operational range beyond 75 cm for long-distance barcode scanning.

Document Workflows

• Improved performance in live video capture environments.
• Consistent document quality through clarity-based frame evaluation.
• Faster MRZ processing for high-throughput identity verification

Developer Notes

• Backward Compatibility – Fully compatible with existing integrations; no code-level changes required for upgrade.
• Configuration Flexibility – Expanded parameter set allows comprehensive model configuration for scenario-specific tuning.
• Production Stability – All new models validated in enterprise environments.

New

• Added a new method, SwitchCapturingTemplate, which allows switching templates dynamically during the image processing workflow.
• Added a new method, ClearDLModelBuffers, to release memory by clearing buffered deep learning models.
• Added a new method, SetGlobalIntraOpNumThreads, to configure the global number of threads used for model execution.
• Added a new method, TakePhoto for capturing photos.
• Added a new button, CameraToggleButton, to the CameraView, allowing users to switch between the front and back cameras. The following APIs are provided for configuring the CameraToggleButton:
  • CameraToggleButton
  • CameraToggleButtonVisible
• Added new methods to class ImageIO for reading and saving images:
  • ReadFromMemory
  • SaveToMemory
• Added new methods to class ImageProcessor for cropping images:
  • CropAndDeskewImage(imageData,quadrilateral,dstWidth,dstHeight,padding)
  • CropAndDeskewImage(imageData,quadrilateral)

11.0.5200 (08/18/2025)

Fixed

• Fixed an xcframework signature issue.

11.0.5100 (08/12/2025)

Fixed

• Small fixes and tweaks.

11.0.3100 (05/30/2025)

Highlights

• Workflow Improvements
  • Restructured the parameter control hierarchy at all levels for finer scope definition and more granular process management, with the stage level newly added.
  • Enabled custom combinations and sequences of sections, increasing flexibility and operational customization under specific conditions.
  • Redesigned document normalization sections to better accommodate diverse document processing operations.
• Deep Learning Integration
  • Improved the reading rate of 1D barcode by introducing a new deblurring deep-learning model.
  • Enhanced text recognition capabilities with deep learning-based text-line recognition.
• Algorithm Enhancements
  • Enabled deduplication at the Region of Interest (ROI) level to consolidate results from multiple tasks.
  • Enhanced the text recognition workflow by integrating improved multi-step recognition processes and validation methods.
  • Improved the CODE_128 and DataMatrix DeepAnalysis algorithms for better decoding accuracy and performance.
  • Added support for new barcode types: CODE_32, MATRIX_25, KIX, and TELEPEN.
  • Added GS1 Application Identifiers (AI) support for improved code parsing capabilities.
• Engineering Optimizations
  • Unified template-loading logic to reduce I/O overhead.
  • Implemented conversion functionality between ImageData and image files, including both on-disk and in-memory files.