Illumination Invariant Character Recognition Using Binarized Gabor Features

George, T.G.; Malmurugan, N.

doi:10.1109/iccima.2007.23

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…[20][21][22]. Figure 3 shows extracted features Table 1 with subimages and collecting directional features from responses, a complete feature vector is obtained, which is depicted in Fig.…”

Section: Average Gabor Features Based Classifiermentioning

confidence: 99%

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Kannada Alphabets Recognition With Application to Braille Translation

Urolagin

Prema²,

Reddy

2011

Int. J. Image Grap.

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In this paper, an effort is made to apply optical character recognition (OCR) for Braille translation on Kannada characters. In general, OCR systems for Indian language are more complex due to larger number of vowels, consonants, and conjuncts and Indian languages are inflectional and agglutinative in nature. Specifically, characters of Kannada script have higher similarity in shape and higher variability across fonts, making recognition of characters a difficult task. A decision tree is developed in this research work. The main motivations are that decision trees provide a natural way to incorporate prior knowledge of domain and many Kannada characters have similar looking shapes. The similar looking characters can be grouped and then further partitioned into categories at various levels to effectively create a decision tree. To facilitate this, three modular classifiers are developed based on the nature of Kannada characters. These modular classifiers are employed at nodes of the decision tree. The Braille equivalent of Kannada characters is obtained by translation rules. An overall accuracy of classification and Braille translation of 93.80% is obtained.

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“…[20][21][22]. Figure 3 shows extracted features Table 1 with subimages and collecting directional features from responses, a complete feature vector is obtained, which is depicted in Fig.…”

Section: Average Gabor Features Based Classifiermentioning

confidence: 99%

“…In our research, we use Gabor filter-based feature extraction technique similar to Refs. [20][21][22] to collect distinguishable features from these subimage space.…”

Section: Average Gabor Features Based Classifiermentioning

confidence: 99%