Abstract Metal stamping character (MSC) automatic identification technology plays an important role in industrial automation. To improve the accuracy and stability of segmentation and recognition for MSCs, an algorithm based on multi-directional illumination image fusion technology is proposed. First, four grayscale images are taken with four bar-shape directional light sources from different directions. Next, based on the difference in surface grayscale characteristics for the different illumination directions of the surface’s stamped depression regions and flat regions, the image background is extracted and eliminated. Second, the images are fused using the difference processing on the images in the two groups of relative illuminant directions. Third, mean filter, binarization, and morphological closing operations are performed on the fused image to locate and segment the character string in the image, and the characters are normalized by correcting the skew of the segmented character string. Finally, histogram of oriented gradient features and a backpropagation neural network algorithm are employed to identify the normalized characters. Experimental results show that the algorithm can effectively eliminate the interference of factors such as oil stains, rust, oxide, shot-blasting pits, and different background colors and enhance the contrast between MSCs and background. The resulting character recognition rate can reach 99.6%.
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