Release Notes for Java Edition - 3.x

3.2.1100 (10/28/2025)

Fixed

• Resolved an initialization crash that occurred when running the SDK within the Spring Boot framework.

3.2.1000 (10/14/2025)

🎉Milestone Release

Version 3.2.1000 introduces a series of AI-driven improvements designed to enhance barcode and MRZ detection accuracy, processing speed, and configuration flexibility.

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

AI-Powered MRZ Detection

• Neural MRZ Localization – The new MRZLocalization model improves region detection accuracy and delivers up to 42.7% faster processing for MRZ-based document workflows.
• Configurable Localization Control – The new LocalizationModes parameter allows configuration for text line detection.

Smart Document Capture

• Clarity-Based Frame Selection – Automatically selects the sharpest and highest-quality frame in live capture workflows.
• Cross-Frame Verification – Updated verification algorithms enhance result reliability.

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.

Changed

Parameter Defaults

• MaxThreadsInOneTask: default changed from 4 → 0 (auto-detection).
• IncludeTrailingCheckDigit: default changed from 1 → 0. Code128 results will no longer include the trailing check digit by default for improved compliance with standard decoding practices.

Deprecations

• DeblurModelNameArray argument of DeblurModes → use ModelNameArray.
• AppendModelBuffer method of CaptureVisionRouter → use AppendDLModelBuffer.

Fixed

• Fixed a crash issue that occurred when calling initLicense.

3.0.6100 (08/19/2025)

Fixed

• Fixed an issue where using a callback function could cause the program to crash.

3.0.6000 (08/06/2025)

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.
  • Added support for capturing data from multi-page files, including PDF and TIFF formats.
  • Implemented conversion functionality between ImageData and image files, including both on-disk and in-memory files.