Release Notes

3.0

First released: 07-04-2023

Highlights

DynamsoftLabelRecognizer SDK has been refactored to integrate with DynamsoftCaptureVision (DCV) architecture, which is newly established to aggregate the features of functional products powered by Dynamsoft. The features are designed to be pluggable, customizable and interactable. In addition, the functional products share the computation so that their processing speed is much higher than working individually.

• DynamsoftCaptureVision architecture consists of:
  • ImageSourceAdapter(ISA), the standard input interface for you to convert image data from different sources into the standard input image data. In addition, ISA incorporates an image buffer management system that allows instant access to the buffered image data.
  • CaptureVisionRouter (CVR), an engine for you to update templates, retrieve images from ISA, coordinate corresponding functional products and dispatch the results to the receivers.
  • Functional products that perform image processing, content understanding and semantic processing. The functional products are pluggable and passively called by CVR when they are required.
  • Result receiver interfaces. You can implement CapturedResultReceiver (CRR) to receive the CapturedResults that output when the processing on an image is finalized. You can also implement IntermediateResultReceiver (IRR) to get timely results from different stages of the workflow.
• The parameter template system has been comprehensively upgraded.
  • Multiple algorithm task settings are available. You can define barcode decoding, label recognizing, document scanning and semantic processing tasks in one template file.
  • Extended the feature of the ROI system. By configuring the target ROI parameters, you can not only specify an ROI on the original image but also define the dependencies of the algorithm tasks. This feature enables you to customize the workflow when processing complex scenarios.
  • The image processing parameters are separated from the task parameters so that the template settings become more clear and concise.
• The intermediate result system has been improved.
  • Achieved the intermediate result sharing between different functional products. The results that have the same image source and processing parameters are directly reused, which speeds up the image processing workflow. You don’t need to add any additional code to enable the intermediate result sharing. The library can recognize all the reusable results automatically based on the template file you uploaded.
  • The readability and interactivity of the intermediate results are enhanced. IntermediateResultReceiver allows you to receive up to 27 different types of Intermediate results. You can clearly read which stage of the algorithm each result is output from. In addition, IntermediateResultManager allows you to intervene in the workflows by modifying the intermediate results.

Versions	Available Editions
3.0.10	C++
3.0.0	C++

2.2

First released: 11-30-2021

Highlights

• Improved the performance of the MRZ scenario. The recognition speed is improved by about 70%.

• Added character normalization modes to normalize the text. This feature will improve the read rate when processing the text in nonstandard fonts like dot matrix.

• New parameter control and recognition methods are added to fit more usage scenarios.

Versions	Available Editions
2.2.30	JavaScript
2.2.20	C/C++ / Android / iOS
2.2.11	JavaScript
2.2.10	C/C++ / JavaScript
2.2.4	JavaScript
2.2.2	C/C++ / JavaScript
2.2.1	JavaScript
2.2.0	C/C++ / JavaScript

2.0

First released: 08-26-2021

Highlights

• Added image processing modes to enhance the usage scenarios like MRZ reading:
  • Texture area detection
  • Color Conversion
  • Binarization
  • Grayscale image processing

• Added dictionary-based correction. The dictionary is minimal and customizable to only cover a scenario-oriented vocabulary.

• Improved the overall performance by replacing Caffe engine with OpenCV dnn engine

• Improved the readability of recognizing skewed characters.

• Improved the MRZ reading performance.

• Added a core library. Migrated the core structs/classes from the LabelRecognizer library to the core library.

Versions	Available Editions
2.0.0	C/C++ / .NET / Java / Android / iOS

 

1.x Versions

 

1.2

First released: 05-18-2021

Highlights

• Added timeout mechanism. DLR checks at a few points whether the elapsed time for the current image is longer than its value. If so, DLR will end the flow. Timeout prevents one image from costing too much time.

• Added parameters for lines filtering:
  • LineStringLengthRange is used to define the minimum and maximum string length when running the recognition process on a specific line.
  • MaxLineCharacterSpacing is used to control the spacing between characters treated as one line.

• Improved the regular expression parameter by supporting more RegEx pattern syntaxes.

• Improved the recognition accuracy when dealing with skewed and italics characters.

• Improved the recognition accuracy for serif fonts.

Versions	Available Editions
1.2.0	C/C++ / .NET / Android / iOS
1.2.1	C/C++ / .NET / Java / Android / iOS

 

1.0

First released: 02-24-2021

Highlights

• Supports text recognition from BMP, JPEG, PNG and single-page TIFF files.
• Supports zonal OCR and provides three ways to localize text areas:
  • Pre-define an area manually in pixel or percentage.
  • Specify an area relative to the barcode zone, which allows you to recognize accompanying texts near the barcode.
  • Specify an area relative to blocks which share the same colour or uses the same font colour. A common example would be a price tag, where the text of interest is always on a yellow square background, the yellow square can serve as the reference region.
• Supports specifying a regular expression to improve recognition accuracy and robustness.

C/C++ / .NET / Android / iOS

Versions	Available Editions
1.0.0	C/C++ / .NET / Android / iOS

 

1.0 Beta

First released: 12-10-2020

Versions	Available Editions
1.0.0 Beta	C/C++