Release Notes for Dynamsoft Barcode Reader - 11.x

11.6 (07/23/2026)

Highlights

Multimodal PDF Content Extraction

• New PDF Reading Mode - Added PDFRM_MULTIMODAL to PDFReadingMode, now the default for PDFReadingParameter. It extracts vector graphics, text content, and embedded images from PDF files, enabling stronger barcode reading performance in multimodal PDF scenarios.
• Performance Behavior Update - Compared with full-page raster rendering, PDFRM_MULTIMODAL usually improves speed by avoiding unnecessary rendering of irrelevant regions.

Multi-Threaded Barcode Decoding

• Get results sooner with parallel processing - Barcode decoding now uses a breadth-first strategy that decomposes a single DBR Task into one Localization Work and one or more Decoding Works. This improves thread utilization and reduces the chance that a slow DeblurMode attempt blocks other faster decoding attempts, helping valid results come back sooner.

DataMatrix Color Inversion Detection

• Handle normal and inverted DataMatrix more efficiently - Added AutoDetectColorInversion to automatically handle both normal and inverted DataMatrix barcodes. Instead of processing the whole image twice, the SDK applies dual-polarity handling only to localized DataMatrix regions, which makes processing faster in dual-polarity scenarios.

Barcode Layout Analysis

• Decode dense grid barcodes more completely - Added LayoutAnalyzer to organize barcode locations into logical line or matrix layouts and infer unrecognized barcode regions when gaps exist, enabling workflows such as fast first-pass decoding, missing-region inference, and targeted second-pass decoding on dense N*M barcode layouts.

Cross-Version License Support

• Use a single license across SDK versions - Full License 1.0 keys (starting with “f”) that are non-perpetual are no longer version-checked, so the same key can be used across SDK versions without reactivation.

11.4 (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 CECISegment class, along with the GetECISegmentsCount() and GetECISegment() methods in the CBarcodeResultItem and CDecodedBarcodeElement 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.

11.2 (10/14/2025)

✨ Key Highlights

AI-Powered Barcode Detection & Decoding

• 🧠 First-to-Market AI Localization: Revolutionary OneDLocalization and DataMatrixQRCodeLocalization neural network models for superior detection of blurred/low-resolution 1D codes and DataMatrix/QR codes with missing or damaged finder patterns
• ⚡ Specialized Decoders: Cutting-edge EAN13Decoder and Code128Decoder models deliver unprecedented accuracy for blurred and long-distance scenarios
• 🔍 Enhanced Clarity Processing: Completely redesigned OneDDeblur model with superior motion blur and focus blur recovery algorithms
• 🎯 Flexible Model Configuration: Advanced ModelNameArray parameter enables on-demand model loading and precise selection for specific barcode scenarios

Precision Control

• ⚙️ Granular Deblur Methods: Fine-tuned DM_DEEP_ANALYSIS with specialized method control - OneDGeneral, TwoDGeneral, and EAN13Enhanced for targeted optimization
• 🎯 Smart Barcode Counting: New ExpectedBarcodesCount parameter enables format-specific quantity control and early termination optimization for known-quantity scenarios
• 🔍 Advanced Region Detection: New RPM_GRAY_CONSISTENCY mode enables precise region detection based on grayscale uniformity and local consistency for document and label processing

11.0 (03/04/2025)

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.
  • Lowered the error rate of 1D and DataMatrix barcode localization using custom deep-learning object detection.
• 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 CImageData and image files, including both on-disk and in-memory files.