DEBAR High-Performance Barcode Reading SDK

Design Goals

Traditional barcode reading equipment often struggles with automation and depth of field limitations, making it challenging to meet advanced automation needs. For instance, barcode scanners require manual operation, alignment with the barcode, and are limited by distance and angle. Although omnidirectional laser scanners overcome directional constraints, they come with high costs, limited depth of field, and restricted reading distances. Recent advancements in CCD sensors and machine vision technology have introduced image processing-based barcode recognition as a viable option, but developing high-performance barcode reading software with this approach involves overcoming several technical hurdles:

  • Barcode Positioning: In industrial environments, complex backgrounds, significant light variations, and differing barcode brightness, orientation, and sizes (with varying distances between the lens and barcode) can make accurate positioning difficult. When the barcode occupies a small portion of the field of view (often less than one-thousandth), locating it becomes challenging.
  • Image Blur and Out-of-Focus Issues: Object movement, optical system errors, and lens depth of field limitations can cause CCD-captured images to be blurred and out of focus, obscuring barcode boundaries. Effective algorithms are needed to mitigate the effects of blur and out-of-focus issues and to precisely calculate the width of the barcode stripes.
  • Defaced Barcodes: Barcode defacement is common in production environments. An effective system must offer fault tolerance and be capable of identifying partially defaced or incomplete barcodes.
  • Processing Capability: Long-distance barcode identification requires high-resolution cameras, with resolution and identification distance increasing dramatically according to a quadratic relationship. Since image processing workload scales with image resolution, suitable algorithms must be implemented to manage computational demands efficiently.

DEBAR aims to deliver a high-performance, cost-effective barcode reading solution leveraging image processing technology.

Features

  • High Performance: Optimized algorithm for real-time barcode recognition through video. Recognition speed varies with resolution—typically 10ms for 1 million pixel images and 40ms for 12 million pixels.
  • Long Range: Utilize inexpensive 3 to 5 million pixel USB cameras to identify small barcodes at distances of 0.8cm x 6cm from 0.8-1.2 meters. With a 16mm lens and 12 million pixel CCD, recognition distance extends to 2 meters, and further with advanced optical systems.
  • Multiple Codes at a Time: Recognize multiple barcodes simultaneously with a single API call (configurable number, with recognition starting from larger to smaller barcode areas).
  • Barcode Types: Supports EAN, UPC, ISBN, CODE-39, Code 25, CODE128-A/CODE128-B/CODE128-C, and many other barcodes.
  • High Recognition Rate: The algorithm handles image blur and out-of-focus issues effectively, enhancing recognition rates. The CODE128 decoding algorithm is optimized for high reliability, even with partially defaced or incomplete barcodes.
  • Simple SDK: Easy-to-use API encapsulated in a single DLL, compatible with C/C++, C#, JAVA, GO.
  • Cross-Platform Support: Compatible with Windows, Linux, and Android systems.

Typical Use

  • Logistics Industry: For express delivery, sorting, and warehouse delivery operations.
  • Warehouse Management

Download Trial

This sample program, developed using DEBAR SDK under Windows, offers a basic demonstration and provides source code for SDK users to reference during their development process.

For additional versions or information, please contact us.

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