Improving Data Matrix mobile recognition via fast Hough transform and adaptive grid extractors

The Data Matrix is a barcode symbology originally designed for industrial needs. Today, its symbols are increasingly found on everyday products such as pharmaceutical packaging, electronic components, food labels, and clothing tags. This widespread usage presents a challenge: reading Data Matrix symbols from images captured by mobile cameras in uncontrolled environments. The reading process mainly consists of three steps, namely barcode localization, segmentation and decoding. In this work, we focus on the precise localization and segmentation of Data Matrix barcodes. We introduce a new method that involves the localization of the finder pattern using fast Hough transform and subsequent iterative segmentation to extract the encoded message. Our approach demonstrates superior localization quality, as measured by the mean Intersection over Union metric (0.889), and achieves better recognition accuracy (0.903) compared to open–source solutions for reading Data Matrix barcodes, such as libdmtx (0.665), ZXing (0.569), and ZXing–cpp (0.858). Our method requires only 35 milliseconds for computations on an ARM device, enabling real–time processing. It is significantly faster than libdmtx (10 seconds), ZXing (610 milliseconds), although it is slightly slower than ZXing–cpp (6.65 milliseconds).