D. Desrochers, Y. Jin, Z. Qu, and A. Saengdeejing
Damaged, character recognition, readability, neural network
Ever since the character strings on silicon wafers were read using OCR (Optical Character Recognition) cameras, there has been a problem with damaged characters. This problem is due to reflection from the light source or physical damage of the characters themselves. There are obvious types of damage that occur frequently on many of the bitmaps that the OCR camera reads. With these types, one can test them to find the most damaging types for each particular character. However, currently there is no known research that systematically determines the worst damages or limits of damage to characters for speciļ¬c OCR methods such as template matching or neural network algorithms. This article presents algorithms for testing common forms of damages on template-matching optical readers reading strings on silicon wafers. It also displays results from combining a simple neural network and the algorithms. The results on readability are critical for the development of robust OCR systems.
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