What happens when the surface for the printing is uneven (color, texture, reflectivity) or perspective distortion causes the shape and/or proportions of the character to fall outside the nominal range set in the parameters? The toolset struggles to properly segment the characters and is confused as to what some of them are. The badly scratched/smudged ‘R’ is even being found by the OCR tool as two separate characters.
Enter Deep Learning OCR tools.
ViDiRead from Cognex leverages deep learning algorithms to decipher badly deformed, skewed, and poorly etched codes using OCR. The In-Sight ViDiRead tool works right out of the box thanks to pre-trained font libraries which dramatically reduce development time. Simply define the region of interest (ROI) and set the character size. In situations where new characters are introduced, you simply capture a handful of images, label the unknown character, and click train.
Using the same first image from before, we can train the ViDiRead tool and it reads as expected. A closeup of the last two characters shows nicely formed characters that the tool has no issues decoding.
Now when we use the image of the damaged label, the ViDiRead tool has absolutely no problem reading the characters. Even though the ‘R’ is poorly printed due to scratching/smudging, ViDiRead does not have any issues reading it.
Traditional OCR tools are great in many applications but there are some extra difficult-to-read texts that just do not allow these tools to be used. When all others have failed, ViDiRead will succeed.
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