Real-time automatic recognition of omnidirectional multiple barcodes and DSP implementation

Barcodes have been extensively adopted in daily life, such as in merchandise labels, inventory control, storage/retrieval systems and inspection. Computer-vision-based barcode recognition can definitely facilitate barcode reading, especially for multiple barcodes and free orientation and in complex scenarios. This work, presents an automatic barcode detection and recognition algorithm for multiple and rotation invariant barcode decoding. The proposed system comprises three stages. First, the barcode is extracted by coarse-to-fine segmentation in four steps: background small clutter reduction, candidate barcode segmentation, barcode verification and barcode rotation and regularization. To enhance the barcode region, thin and small background noise clusters are eliminated using Max–Min Differencing. The approach combines several image-processing schemes, namely Gaussian smoothing filtering, connected component analysis, orientation homogeneity, moment analysis and iterative thresholding. The second stage decodes the barcode by scanning multiple traversal lines, thus preventing decoding errors due to minor barcode defects. Finally, the proposed system is implemented and optimized on a DM6437 DSP EVM board. Experimental results indicate that the proposed approach can locate multiple and omnidirectional barcodes, even with a complex background and minor distortion. The recognition rates for 10,395 lottery barcodes and 388 merchandise barcodes are 99.74 and 90.7%, respectively. The proposed system is promising and has been successfully adopted in commercial applications of lottery reading and verification of winning numbers.