There are several License plate recognition methods
that have been used before, such as morphological operations
, edge extraction , combination of gradient
features , salience features , a neural network for
color or grayscale classification, and vector
quantization . The idea behind the same base papers are
adopted in the Meter Reading Recognition process.
Due to ambient lighting conditions, interference
characters, and other problems, it is difficult to detect Values
in the Meter in complex conditions. Some of previous
License plate detection methods are restricted to work under
certain conditions, such as fixed backgrounds and known
color .
There are several License plate recognition methodsthat have been used before, such as morphological operations, edge extraction , combination of gradientfeatures , salience features , a neural network forcolor or grayscale classification, and vectorquantization . The idea behind the same base papers areadopted in the Meter Reading Recognition process.Due to ambient lighting conditions, interferencecharacters, and other problems, it is difficult to detect Valuesin the Meter in complex conditions. Some of previousLicense plate detection methods are restricted to work undercertain conditions, such as fixed backgrounds and knowncolor .
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