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## 3.2.1000 (10/14/2025)
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🎉 **Milestone Release** - This version introduces groundbreaking AI-powered enhancements that significantly improve accuracy and performance across all barcode and MRZ processing scenarios.
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### 🎉Milestone Release
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Version 3.2.1000 introduces a series of AI-driven improvements designed to enhance barcode and MRZ detection accuracy, processing speed, and configuration flexibility.
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This release focuses on practical performance gains for production environments across retail, logistics, manufacturing, and identity verification workflows.
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### ✨ Key Highlights
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#### AI-Powered Barcode Detection and Decoding
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- New Localization Models – Introduces [`OneDLocalization`]({{ site.dcvb_parameters_reference }}barcode-reader-task-settings/localization-modes.html#modelnamearray) and [`DataMatrixQRCodeLocalization`]({{ site.dcvb_parameters_reference }}barcode-reader-task-settings/localization-modes.html#modelnamearray) neural network models for improved detection of **blurred / low-resolution 1D codes**, or **partially damaged DataMatrix/QR codes**.
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- Specialized Decoders – Adds [`EAN13Decoder`]({{ site.dcvb_parameters_reference }}barcode-reader-task-settings/deblur-modes.html#modelnamearray) and [`Code128Decoder`]({{ site.dcvb_parameters_reference }}barcode-reader-task-settings/deblur-modes.html#modelnamearray) models optimized for **long-distance** and **motion-blurred** decoding scenarios.
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- Redesigned Deblur Model – The [`OneDDeblur`]({{ site.dcvb_parameters_reference }}barcode-reader-task-settings/deblur-modes.html#modelnamearray) model now provides more effective recovery from **motion and focus blur**.
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- Configurable Model Selection – The new `ModelNameArray` parameter supports flexible model loading and fine-grained control for specific barcode types.
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**AI-Powered Barcode Detection & Decoding**
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- 🧠 **First-to-Market AI Localization**: Revolutionary [`OneDLocalization`]({{ site.dcvb_parameters_reference }}barcode-reader-task-settings/localization-modes.html#modelnamearray) and [`DataMatrixQRCodeLocalization`]({{ site.dcvb_parameters_reference }}barcode-reader-task-settings/localization-modes.html#modelnamearray) neural network models for superior detection of **blurred/low-resolution 1D codes** and **DataMatrix/QR codes with missing or damaged finder patterns**
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- ⚡ **Specialized Decoders**: Cutting-edge [`EAN13Decoder`]({{ site.dcvb_parameters_reference }}barcode-reader-task-settings/deblur-modes.html#modelnamearray) and [`Code128Decoder`]({{ site.dcvb_parameters_reference }}barcode-reader-task-settings/deblur-modes.html#modelnamearray) models deliver unprecedented accuracy for **blurred and long-distance** scenarios
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- 🔍 **Enhanced Clarity Processing**: Completely redesigned [`OneDDeblur`]({{ site.dcvb_parameters_reference }}barcode-reader-task-settings/deblur-modes.html#modelnamearray) model with superior **motion blur and focus blur** recovery algorithms
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- 🎯 **Flexible Model Configuration**: Advanced `ModelNameArray` parameter enables on-demand model loading and precise selection for specific barcode scenarios
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#### Precision and Processing Control
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**Precision Control**
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- ⚙️ **Granular Deblur Methods**: Fine-tuned [`DM_DEEP_ANALYSIS`]({{ site.dcvb_parameters_reference }}barcode-reader-task-settings/deblur-modes.html#dm_deep_analysis) with specialized method control - `OneDGeneral`, `TwoDGeneral`, and `EAN13Enhanced` for targeted optimization
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- 🎯 **Smart Barcode Counting**: New [`ExpectedBarcodesCount`]({{ site.dcvb_parameters_reference }}barcode-format-specification/expected-barcodes-count.html) parameter enables **format-specific quantity control** and **early termination optimization** for known-quantity scenarios
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- 🔍 **Advanced Region Detection**: New [`RPM_GRAY_CONSISTENCY`]({{ site.dcvb_parameters_reference }}image-parameter/region-predetection-modes.html#rpm_gray_consistency) mode enables precise region detection based on **grayscale uniformity** and **local consistency** for document and label processing
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- Enhanced Deblur Methods – [`DM_DEEP_ANALYSIS`]({{ site.dcvb_parameters_reference }}barcode-reader-task-settings/deblur-modes.html#dm_deep_analysis) now includes sub-level control with `OneDGeneral`, `TwoDGeneral`, and `EAN13Enhanced` options.
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- Barcode Count Expectation – The new [`ExpectedBarcodesCount`]({{ site.dcvb_parameters_reference }}barcode-format-specification/expected-barcodes-count.html) parameter enables **format-specific quantity control** and **early termination** in fixed-count workflows.
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- Improved Region Detection – The new [`RPM_GRAY_CONSISTENCY`]({{ site.dcvb_parameters_reference }}image-parameter/region-predetection-modes.html#rpm_gray_consistency) mode provides more precise region extraction based on **grayscale uniformity** and **local consistency** for document and label processing.
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#### AI-Powered MRZ Detection
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**Enhanced Text Processing**
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- 🚀 **High-Speed and Precise MRZ Region Detection**: Revolutionary neural network [`MRZLocalization`]({{ site.dcvb_parameters_reference }}label-recognizer-task-settings/localization-modes.html#modelnamearray) model delivers **42.7% faster processing** with enhanced region detection accuracy for passport and ID workflows
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- 🎛️ **Advanced Localization**: New [`LocalizationModes`]({{ site.dcvb_parameters_reference }}label-recognizer-task-settings/localization-modes.html) parameter provides unprecedented control over text line detection algorithms
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- Neural MRZ Localization – The new [`MRZLocalization`]({{ site.dcvb_parameters_reference }}label-recognizer-task-settings/localization-modes.html#modelnamearray) model improves region detection accuracy and delivers up to **42.7%** faster processing for MRZ-based document workflows.
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- Configurable Localization Control – The new [`LocalizationModes`]({{ site.dcvb_parameters_reference }}label-recognizer-task-settings/localization-modes.html) parameter allows configuration for text line detection.
- 🔄 **Enhanced Cross-Frame Verification**: Advanced cross-frame verification algorithms significantly improve result reliability and accuracy
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- Clarity-Based Frame Selection – Automatically selects the sharpest and highest-quality frame in live capture workflows.
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- Cross-Frame Verification – Updated verification algorithms enhance result reliability.
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### Performance Highlights
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###💡 What This Means for You
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#### Barcode Workflows
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**For Challenging Barcode Scenarios**
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-**Blurred conditions**: 26.5% better read rates with 44% faster processing - ideal for handheld scanning and moving objects
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-**Extended distance capability**: Breakthrough support for reading distances beyond 75cm - revolutionizing warehouse automation and high-shelf scanning
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-**Damaged 2D codes**: Enhanced detection of DataMatrix and QR codes with missing or damaged finder patterns - perfect for manufacturing and logistics applications
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- Up to **26.5%** higher read rates under blur conditions with as much as **44%** faster processing.
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- Reliable decoding of DataMatrix and QR codes with missing or damaged finder patterns.
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- Extended operational range beyond 75 cm for long-distance barcode scanning.
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**For Document Processing Applications**
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-**Real-time video streams**: Optimized performance maintains smooth user experience in live capture scenarios
-**Retail environments**: Enhanced performance for blurred handheld scanning and long-distance shelf reading
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-**Logistics & shipping**: Improved recognition for package tracking with better blur and long-distance scanning capabilities
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-**Manufacturing QC**: Improved 2D code reading on printed/etched parts with wear damage
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-**Security applications**: Faster MRZ processing for high-throughput identity verification
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- Improved performance in live video capture environments.
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- Consistent document quality through clarity-based frame evaluation.
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- Faster MRZ processing for high-throughput identity verification
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**For Developers**
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-**Backward Compatible**: Seamless upgrade with existing code and easy migration path
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-**Flexible Configuration**: Extensive parameter customization for specific use cases and comprehensive model configuration options
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-**Enterprise Ready**: Battle-tested stability for production environments
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### Developer Notes
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- Backward Compatibility – Fully compatible with existing integrations; no code-level changes required for upgrade.
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- Configuration Flexibility – Expanded parameter set allows comprehensive model configuration for scenario-specific tuning.
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- Production Stability – All new models validated in enterprise environments.
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### Changed
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- Updated the default value of parameter [`MaxThreadsInOneTask`]({{ site.dcvb_parameters_reference }}barcode-reader-task-settings/max-threads-in-one-task.html) from 4 to 0 (auto-detection).
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- Updated the default value of parameter [`IncludeTrailingCheckDigit`]({{ site.dcvb_parameters_reference }}barcode-format-specification/include-trailing-check-digit.html) from 1 to 0. This means Code128 barcode results will no longer include the trailing check digit in the decoded bytes by default, improving compatibility with industry standards.
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- Deprecated argument `DeblurModelNameArray` of parameter [`DeblurModes`]({{ site.dcvb_parameters_reference }}barcode-reader-task-settings/deblur-modes.html), use `ModelNameArray` instead.
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- Deprecated method `AppendModelBuffer` of class [`CaptureVisionRouter`]({{ site.dcvb_cpp_api }}capture-vision-router/capture-vision-router.html), use [`AppendDLModelBuffer`]({{ site.dcvb_cpp_api }}capture-vision-router/auxiliary-methods.html#appenddlmodelbuffer) instead.
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#### Parameter Defaults
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-[`MaxThreadsInOneTask`]({{ site.dcvb_parameters_reference }}barcode-reader-task-settings/max-threads-in-one-task.html): default changed from 4 → 0 (auto-detection).
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-[`IncludeTrailingCheckDigit`]({{ site.dcvb_parameters_reference }}barcode-format-specification/include-trailing-check-digit.html): default changed from 1 → 0. Code128 results will no longer include the trailing check digit by default for improved compliance with standard decoding practices.
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### Deprecations
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-`DeblurModelNameArray` argument of [`DeblurModes`]({{ site.dcvb_parameters_reference }}barcode-reader-task-settings/deblur-modes.html) → use `ModelNameArray`.
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-`AppendModelBuffer` method of `CaptureVisionRouter` → use [`AppendDLModelBuffer`]({{ site.dcvb_cpp_api }}capture-vision-router/auxiliary-methods.html#appenddlmodelbuffer).
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