Ultra-High Density Decoding of 2D Matrix Barcodes Eugene P. Gerety Dr. Khaled M. Elleithy Department of Computer Science and Engineering University of Bridgeport, Bridgeport, CT Problem Research Objectives Any combination of printing and imaging technologies has inherent limitations on maximum achievable 2D-matrix barcode density. Existing decoding techniques rely heavily on code-specific “helper features” clock tracks, and ECC. This means: Decoder cannot function without “helper” features “Helper” damage renders code unreadable Maximum code density is limited “Blind" Decoding. Code-agnostic “bit picking” without reliance on helper features or error correction coding “Alias disambiguation” at code densities high enough to produce aliasing in the edge image “Bit Modeling” to compensate for inherent linear and nonlinear behaviors of the printing and imaging processes Methodology Establish Bit Grid Metrics, Identify Bit Centers Perform directional edge detection on “patch” Take 2D CFFT of edge-detected image patch Identify primary grid peaks and all possible aliases Perform rule and geometry-based alias disambiguation “Unfold” aliases to determine “true” grid peaks. Take geometric center of pixels around grid peaks From values (6) determine grid spacing (frequency) and bit centers (phase) Spline grid data from multiple “patches” distortions, etc. to refine grid metrics 1 2 3 - 6 Development of Bit Model DECODED! 2 4 - 7 Imaging Model Incoming light is masked by printed bits Light passing through bits is scattered in medium Returning light is masked again by bits MTF of camera 1 Bit Modeling and Decoding Estimate system MTF, PSF, ink spread for imaging model. From imaging model, develop a Bit Model” for the extent of a bit’s influence on the grayscale values of nearby pixels. (optional) Determine extent of code by searching for “quiet area” around code Apply bit model across code image, correcting expected grayscale values for influence of neighboring bits Assign bit values for high-confidence bits (start with large white or black areas of low ambiguity) As more bit values are assigned, readjust bit-center gray values and assign additional high-confidence bit values. Continue assigning bit values and improving bit-center grayscale estimates until all bit values have been assigned Conclusion This technique provides excellent, low-error-rate recovery of raw 2D matrix bit fields, even in the presence of edge-aliasing. The bit modeling techniques further assist in decoding high-density codes by accounting for pixel gray levels at low sample rates where the pixels may not be well aligned to the bit grid. In the example shown here, 100% bit accuracy was achieved without the use of error correction. The technique is well-suited to a wide variety of 2D code types REFERENCES Gaur, Priyanka, and Shamik Tiwari. "Recognition of 2D Barcode Images Using Edge Detection and Morphological Operation." International Journal of Computer Science and Mobile Computing 3.4 (2014): 1277-1282 Pârvu, Ovidiu, and Andrei G. Balan. "A method for fast detection and decoding of specific 2d barcodes." Proceedings of the 17th Telecommunications forum TELFOR. 2009. Chandler D.G., Batterman E.P., and Shah G, "Hexagonal, information encoding article, process and system" U.S. Patent 4,874,936, issued Oct 17, 1989 Yule J.A.G. and Neilsen W.J. "The Penetration Of Light Into Paper And Its Effect On Halftone Reproduction" TAGA Proceeding 3, 65-76, 1951.