Recognition Quality and Speed

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This documentation part provides a detailed description of various recognition quality settings that can be customized to optimize the accuracy and speed of barcode reading in Aspose.BarCode for C++.

Barcode recognition is based on machine vision techniques and utilizes sophisticated mathematical algorithms for object detection and reading. Similar to other computer vision applications, converting an arbitrary image into a machine-readable code strongly depends on the quality of the source image. Namely, barcode images with low quality may be deemed unreadable according to accepted standards. Various methods can be used to recognize barcodes even with unacceptable quality; however, using these methods requires additional CPU computation time and leads to considerably increasing overall reading time.

Aspose.BarCode for C++ allows optimizing the barcode recognition process in terms of speed and quality depending on particular business needs and specificities. The library provides a special class called QualitySettings that can be used to implement flexible settings for barcode recognition and to reach the required trade-off between reading capacity and speed according to the quality of source barcode images.

Article Description
Recognition Modes Gives an overview of available modes to optimize barcode reading quality and speed
Recognition Quality Presets Gives an overview of supported presets to manage reading quality settings
Read Damaged Barcodes Describes special quality settings and median filtering options that can be used to improve recognition results in cases of various distortions barcode images, such as Gaussian noise, white spots, visual artifacts, erased bars, severely corrupted QR Codes, and others
Read Non-typical Barcodes Describes special quality settings that can be used to improve recognition results in cases of non-typical barcode images, such as colored barcodes on a colored background, inverted images, or industrial Data Matrix barcodes
Detect Potential Barcode Regions Explains different ways to detect potential barcodes in an image