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Swift
Objective-C

Release Notes for Java Edition - 11.x

11.4.1000 (02/05/2026)

Highlights

AI-Powered Barcode Detection and Decoding

  • PDF417 Localization Model – Introduces the PDF417Localization neural network model for improved detection of PDF417 barcodes, especially under challenging conditions.

  • Code39/ITF Decoding Model – Adds the Code39ITFDecoder model for enhanced decoding of Code 39 and ITF barcodes under blurred or low-resolution conditions.

  • Deblur Models for 2D Barcodes – Adds the DataMatrixQRCodeDeblur and PDF417Deblur models to provide more effective recovery from motion and focus blur for DataMatrix, QR Code, and PDF417 barcodes.

ECI (Extended Channel Interpretation) Support

  • ECI Information Return – Adds support for retrieving Extended Channel Interpretation (ECI) data from barcodes. The new ECISegment class, along with the getECISegments() method in the BarcodeResultItem and DecodedBarcodeElement classes, enables access to character encoding information embedded in barcodes.

  • ECI-Based Text Interpretation – Adds support for interpreting ECI segments during barcode decoding, improving compatibility with international character sets.

Performance Improvements

  • On-Demand Model Loading – Implements lazy loading for AI models, reducing initialization time by loading models only when first needed.

  • Smart Model Selection – Models are now loaded based on configured barcode formats, minimizing memory usage by excluding unused models.

  • Improved Confidence Scoring – Enhances confidence score calculation for results from neural network models, providing more accurate quality indicators.

  • DPM Barcode Optimization – Improves recognition rate for Direct Part Marking (DPM) barcodes commonly used in industrial and manufacturing environments.

New

Changed

  • captureMultiPages now returns results sorted by page number.

  • Barcode text encoding fallback changed from UTF-8 to ISO-8859-1 when no ECI information is present in the barcode.

  • Updated default value of compensation parameter in ImageProcessor.convertToBinaryLocal() from 0 to 10.

  • convertToBinaryGlobal() and convertToBinaryLocal() of ImageProcessor class now support color, binary and grayscale images as input.

Improved

  • Improved license binding stability on macOS devices.

Removed

Fixed

  • Fixed incorrect coordinate in barcode result when using neural network models with a specified region.

  • Fixed crash and hang issues that could occur in certain scenarios.

  • Fixed various minor bugs and improved overall stability.

11.2.5000 (12/16/2025)

This release includes security maintenance updates to ensure continued protection of the product.

Security Updates

  • Updated third-party libraries to incorporate the latest security fixes.

Bug Fixes

  • Fixed memory leak, crash, and hang issues in various scenarios.
  • Improved stability in multi-threading operations.

11.2.1100 (10/28/2025)

Fixed

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

11.2.1000 (10/14/2025)

🎉Milestone Release

Version 11.2.1000 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.

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

Fixed

  • Fixed a crash issue that occurred when calling initLicense.

11.0.6100 (08/19/2025)

Fixed

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

11.0.6000 (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.
  • Deep Learning Integration
    • Improved the reading rate of 1D barcode by introducing a new deblurring deep-learning model.
  • Algorithm Enhancements
    • Enabled deduplication at the Region of Interest (ROI) level to consolidate results from multiple tasks.
    • 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.
  • 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.

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