Character Recognition in Natural Scenes Using Convolutional Co-occurrence HOG

Recognition of characters in natural images is a challenging task due to the complex background, variations of text size and perspective distortion, etc. Traditional optical character recognition (OCR) engine cannot perform well on those unconstrained text images. A novel technique is proposed in th...

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Bibliographic Details
Main Authors: Bolan Su, Shijian Lu, Shangxuan Tian, Joo Hwee Lim, Chew Lim Tan
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Character recognition
co-occurrence HOG
Feature extraction
Histogram of Oriented Gradient
Image segmentation
Optical character recognition software
Scene Text Recognition
Testing
Text recognition
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Summary:Recognition of characters in natural images is a challenging task due to the complex background, variations of text size and perspective distortion, etc. Traditional optical character recognition (OCR) engine cannot perform well on those unconstrained text images. A novel technique is proposed in this paper that makes use of convolutional cooccurrence histogram of oriented gradient (ConvCoHOG), which is more robust and discriminative than both the histogram of oriented gradient (HOG) and the co-occurrence histogram of oriented gradients (CoHOG). In the proposed technique, a more informative feature is constructed by exhaustively extracting features from every possible image patches within character images. Experiments on two public datasets including the ICDAr 2003 Robust Reading character dataset and the Street View Text (SVT) dataset, show that our proposed character recognition technique obtains superior performance compared with state-of-the-art techniques.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2014.504