Decision Tree Based Recognition of Bangla Text from Outdoor Scene Images
Identifieur interne : 000490 ( Main/Exploration ); précédent : 000489; suivant : 000491Decision Tree Based Recognition of Bangla Text from Outdoor Scene Images
Auteurs : Ranjit Ghoshal [Inde] ; Anandarup Roy [Inde] ; Kumar Bhowmik [Pays-Bas] ; K. Parui [Inde]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.
Abstract
Abstract: This article proposes a scheme for automatic recognition of Bangla text extracted from outdoor scene images. For extraction, we obtain the headline, then apply certain conditions to distinguish between text and non-text. By removing the headline we partition the text into two zones. We further observe an association among the text symbols in these two different zones. For recognition purpose, we design a decision tree classifier with Multilayer Perceptron (MLP) at leaf nodes. The root node takes into account all possible text symbols. Further nodes highlight distinguishable features and act as two-class classifiers. Finally, at leaf nodes, a few text symbols remain, that are recognized using MLP classifiers. The association between the two zones makes recognition simpler and efficient. The classifiers are trained using about 7100 samples of 52 classes. Experiments are performed on 250 images (200 scene images and 50 scanned images).
Url:
DOI: 10.1007/978-3-642-24965-5_61
Affiliations:
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For extraction, we obtain the headline, then apply certain conditions to distinguish between text and non-text. By removing the headline we partition the text into two zones. We further observe an association among the text symbols in these two different zones. For recognition purpose, we design a decision tree classifier with Multilayer Perceptron (MLP) at leaf nodes. The root node takes into account all possible text symbols. Further nodes highlight distinguishable features and act as two-class classifiers. Finally, at leaf nodes, a few text symbols remain, that are recognized using MLP classifiers. The association between the two zones makes recognition simpler and efficient. The classifiers are trained using about 7100 samples of 52 classes. Experiments are performed on 250 images (200 scene images and 50 scanned images).</div></front></TEI><affiliations><list><country><li>Inde</li><li>Pays-Bas</li></country><region><li>Groningue (province)</li></region><settlement><li>Groningue (ville)</li></settlement><orgName><li>Université de Groningue</li></orgName></list><tree><country name="Inde"><noRegion><name sortKey="Ghoshal, Ranjit" sort="Ghoshal, Ranjit" uniqKey="Ghoshal R" first="Ranjit" last="Ghoshal">Ranjit Ghoshal</name></noRegion><name sortKey="Parui, K" sort="Parui, K" uniqKey="Parui K" first="K." last="Parui">K. Parui</name><name sortKey="Parui, K" sort="Parui, K" uniqKey="Parui K" first="K." last="Parui">K. 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