Character recognition
is a study that has been used in various fields for many years. In character
recognition, the aim is to identify the various texts, letters and symbols in
the images as accurately and quickly as possible. In addition to the Optical
Character Recognition (OCT) method, which is used as a very common method,
there are many feature extraction methods in which character image features are
compared. In this study, which is presented as another feature extraction
method, the letters on the license plates are recognized. The characters were
examined using the circular shape histogram technique and histograms were
obtained from the sectors within the circular regions. Feature vectors for
letter characters were created using character pixel densities in sectors.
Feature vectors are analyzed linearly and an alternative quick character
recognition method is presented. With the proposed method, the element numbers
of the feature vectors are kept constant. In this way, both the processing
speed is increased and the processing speed variations are minimized. The
results show that the proposed method requires lesser parameters than the OCT
method, but also has a significant success rate according to known feature
extraction methods.
Character recognition
is a study that has been used in various fields for many years. In character
recognition, the aim is to identify the various texts, letters and symbols in
the images as accurately and quickly as possible. In addition to the Optical
Character Recognition (OCT) method, which is used as a very common method,
there are many feature extraction methods in which character image features are
compared. In this study, which is presented as another feature extraction
method, the letters on the license plates are recognized. The characters were
examined using the circular shape histogram technique and histograms were
obtained from the sectors within the circular regions. Feature vectors for
letter characters were created using character pixel densities in sectors.
Feature vectors are analyzed linearly and an alternative quick character
recognition method is presented. With the proposed method, the element numbers
of the feature vectors are kept constant. In this way, both the processing
speed is increased and the processing speed variations are minimized. The
results show that the proposed method requires lesser parameters than the OCT
method, but also has a significant success rate according to known feature
extraction methods.
Primary Language | English |
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Subjects | Engineering |
Journal Section | Research Article |
Authors | |
Publication Date | December 1, 2019 |
Submission Date | July 18, 2018 |
Published in Issue | Year 2019 Volume: 22 Issue: 4 |
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